This file is used to prepare the figures for the paper.

library(dplyr)
library(patchwork)
library(ggplot2)
library(ComplexHeatmap)
library(org.Mm.eg.db)

.libPaths()
## [1] "/usr/local/lib/R/library"

Preparation

Here are the folders where analyzes are stored :

data_dir = "./.."
list.files(data_dir)
##  [1] "0_intro"      "1_metadata"   "2_individual" "3_combined"   "4_zoom"      
##  [6] "5_wu"         "6_figures"    "LICENSE"      "index.html"   "index_layout"

We load the dataset containing all cells :

sobj = readRDS(paste0(data_dir, "/3_combined/hs_hd_sobj.rds"))
sobj
## An object of class Seurat 
## 20003 features across 12111 samples within 1 assay 
## Active assay: RNA (20003 features, 2000 variable features)
##  6 dimensional reductions calculated: RNA_pca, RNA_pca_38_tsne, RNA_pca_38_umap, harmony, harmony_38_umap, harmony_38_tsne

These are all the samples analyzed :

sample_info = readRDS(paste0(data_dir, "/1_metadata/hs_hd_sample_info.rds"))

# Nb cells by dataset
to_plot = table(sobj$sample_identifier) %>%
  as.data.frame.table(., stringsAsFactors = FALSE) %>%
  `colnames<-`(c("sample_identifier", "nb_cells")) %>%
  dplyr::left_join(x = ., y = sample_info, by = "sample_identifier") 

# patchwork
plot_list = aquarius::fig_plot_gb(to_plot, title = "Available datasets")
patchwork::wrap_plots(plot_list) +
  patchwork::plot_layout(design = "A\nB", heights = c(0.1,5)) &
  ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold", size = 15))

These are the custom colors for cell populations :

color_markers = readRDS(paste0(data_dir, "/1_metadata/hs_hd_color_markers.rds"))
color_markers = color_markers[names(color_markers) != "melanocytes"]
ors_color = color_markers["ORS"]
color_markers["ORS"] = color_markers["IFE"] 
color_markers["IFE"] = ors_color
color_markers["B cells"] = "chocolate3"
rm(ors_color)

# re-order
color_markers = color_markers[c("CD4 T cells", "CD8 T cells", "Langerhans cells", "macrophages", "B cells",
                                "cuticle", "cortex", "medulla", "IRS", "proliferative",
                                "HFSC", "ORS", "IBL", "IFE", "sebocytes")]

data.frame(cell_type = names(color_markers),
           color = unlist(color_markers)) %>%
  ggplot2::ggplot(., aes(x = cell_type, y = 0, fill = cell_type)) +
  ggplot2::geom_point(pch = 21, size = 5) +
  ggplot2::scale_fill_manual(values = unlist(color_markers), breaks = names(color_markers)) +
  ggplot2::theme_classic() +
  ggplot2::theme(legend.position = "none",
                 axis.line = element_blank(),
                 axis.title = element_blank(),
                 axis.ticks = element_blank(),
                 axis.text.y = element_blank(),
                 axis.text.x = element_text(hjust = 1, angle = 20))

We define custom colors for sample type :

sample_type_colors = setNames(nm = levels(sample_info$sample_type),
                              c("#C55F40", "#2C78E6"))

data.frame(cell_type = names(sample_type_colors),
           color = unlist(sample_type_colors)) %>%
  ggplot2::ggplot(., aes(x = cell_type, y = 0, fill = cell_type)) +
  ggplot2::geom_point(pch = 21, size = 5) +
  ggplot2::scale_fill_manual(values = unlist(sample_type_colors), breaks = names(sample_type_colors)) +
  ggplot2::theme_classic() +
  ggplot2::theme(legend.position = "none",
                 axis.line = element_blank(),
                 axis.title = element_blank(),
                 axis.ticks = element_blank(),
                 axis.text.y = element_blank())

We set a background color :

bg_color = "gray94"

This is the correspondence between cell types and cell families, and custom colors to color cells by cell family :

custom_order_cell_type = data.frame(
  cell_type = names(color_markers),
  cell_family = c(rep("immune cells", 5),
                  rep("matrix", 5),
                  rep("non matrix", 5)),
  stringsAsFactors = FALSE)
custom_order_cell_type$cell_type = factor(custom_order_cell_type$cell_type,
                                          levels = custom_order_cell_type$cell_type)
rownames(custom_order_cell_type) = custom_order_cell_type$cell_type

family_color = c("immune cells" = "slateblue1",
                 "matrix" = "mediumseagreen",
                 "non matrix" = "firebrick3")

We load markers to display on a dotplot to assess cell type annotation :

dotplot_markers = readRDS(paste0(data_dir, "/1_metadata/hs_hd_dotplot_markers.rds"))
dotplot_markers = dotplot_markers[names(dotplot_markers) != "melanocytes"]
lengths(dotplot_markers)
##      CD4 T cells      CD8 T cells Langerhans cells      macrophages 
##                2                2                2                2 
##          B cells          cuticle           cortex          medulla 
##                2                2                2                2 
##              IRS    proliferative              IBL              ORS 
##                2                2                2                2 
##              IFE             HFSC        sebocytes 
##                2                2                2

Custom functions to display gene expression on the heatmap :

color_fun = function(one_gene) {
  gene_range = range(ht_annot[, one_gene])
  gene_palette = circlize::colorRamp2(colors = c("#FFFFFF", aquarius::color_gene[-1]),
                                      breaks = seq(from = gene_range[1], to = gene_range[2],
                                                   length.out = length(aquarius::color_gene)))
  return(gene_palette)
}

All samples

Settings

This is the projection name to visualize cells :

name2D = "harmony_38_tsne"
name2D_atlas = name2D

Preparation

We make a low resolutive clustering for the heatmap :

sobj = Seurat::FindClusters(sobj, resolution = 0.4)
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 12111
## Number of edges: 475472
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9421
## Number of communities: 17
## Elapsed time: 1 seconds
length(levels(sobj$seurat_clusters))
## [1] 17

We define cluster type and cluster family :

sobj$cell_type = sobj$cell_type %>%
  as.character() %>%
  factor(., levels = names(color_markers))

cluster_type = table(sobj$cell_type, sobj$seurat_clusters) %>%
  prop.table(., margin = 2) %>%
  apply(., 2, which.max)
cluster_type = setNames(nm = names(cluster_type),
                        levels(sobj$cell_type)[cluster_type])

sobj$cluster_type = cluster_type[sobj$seurat_clusters]
sobj$cluster_type = factor(sobj$cluster_type,
                           levels = levels(sobj$cell_type))
sobj$cluster_family = custom_order_cell_type[sobj$cluster_type, "cell_family"]
sobj$cluster_family = factor(sobj$cluster_family,
                             levels = names(family_color))

Figures

Project name :

# Random order
set.seed(1234)
rnd_order = sample(colnames(sobj), replace = FALSE, size = ncol(sobj))

# Extract coordinates
cells_coord = sobj@reductions[[name2D]]@cell.embeddings %>%
  as.data.frame() %>%
  `colnames<-`(c("Dim1", "Dim2"))
cells_coord$project_name = sobj$project_name
cells_coord = cells_coord[(rnd_order), ]

# Plot
ggplot2::ggplot(cells_coord, aes(x = Dim1, y = Dim2, col = project_name)) +
  ggplot2::geom_point(size = 0.5) +
  ggplot2::scale_color_manual(values = sample_info$color,
                              breaks = sample_info$project_name) +
  ggplot2::theme_void() +
  ggplot2::theme(aspect.ratio = 1,
                 legend.position = "none")

Sample type :

# Extract coordinates
cells_coord = sobj@reductions[[name2D]]@cell.embeddings %>%
  as.data.frame() %>%
  `colnames<-`(c("Dim1", "Dim2"))
cells_coord$sample_type = sobj$sample_type
cells_coord = cells_coord[(rnd_order), ]

# Plot
ggplot2::ggplot(cells_coord, aes(x = Dim1, y = Dim2, col = sample_type)) +
  ggplot2::geom_point(size = 0.5) +
  ggplot2::scale_color_manual(values = sample_type_colors,
                              breaks = names(sample_type_colors)) +
  ggplot2::theme_void() +
  ggplot2::theme(aspect.ratio = 1,
                 legend.position = "none")

Cluster :

grey_palette = setNames(nm = levels(sobj$seurat_clusters),
                        rep("#D9D9D9", length(levels(sobj$seurat_clusters))))
grey_palette[c("7", "16", "1", "12", "11", "10", "15")] = "#BDBDBD"
grey_palette[c("16", "14", "5", "9")] = "#969696"

Seurat::DimPlot(sobj, reduction = name2D, pt.size = 0.4,
                group.by = "seurat_clusters", cols = grey_palette,
                label = TRUE, label.size = 6) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() +
  Seurat::NoLegend()

Cell type annotation :

Seurat::DimPlot(sobj, reduction = name2D, pt.size = 0.5,
                group.by = "cell_type", cols = color_markers) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() +
  Seurat::NoLegend()

Cluster family annotation :

Seurat::DimPlot(sobj, reduction = name2D, pt.size = 0.5,
                group.by = "cluster_family", cols = family_color) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() +
  Seurat::NoLegend()

Cell type annotation split by condition :

plot_list = aquarius::plot_split_dimred(sobj, reduction = name2D,
                                        group_by = "cell_type",
                                        group_color = color_markers,
                                        split_by = "sample_type",
                                        split_color = sample_type_colors,
                                        bg_color = bg_color)

patchwork::wrap_plots(plot_list) &
  Seurat::NoLegend()

Gene expression to assess annotation :

genes = c("PTPRC", "MSX2", "KRT14")
names(genes) = c("immune cells", "matrix cells", "non-matrix cells")

plot_list = lapply(c(1:length(genes)), FUN = function(gene_id) {
  gene = genes[[gene_id]]
  pop = names(genes)[gene_id]
  
  sobj$my_gene = Seurat::FetchData(sobj, gene)[, 1] %>%
    aquarius::run_rescale(., new_min = 0, new_max = 10)
  
  Seurat::FeaturePlot(sobj, features = "my_gene", reduction = name2D) +
    ggplot2::scale_color_gradientn(colors = aquarius::color_gene,
                                   breaks = seq(0, 10, by = 2.5),
                                   labels = c("min", rep("", 3), "max")) +
    ggplot2::labs(title = gene) + 
    # subtitle = pop) +
    ggplot2::theme(aspect.ratio = 1,
                   plot.title = element_text(hjust = 0.5, size = 17),
                   plot.subtitle = element_text(hjust = 0.5, size = 15),
                   legend.text = element_text(size = 15),
                   legend.position = "none") +
    Seurat::NoAxes()
})

plot_list
## [[1]]

## 
## [[2]]

## 
## [[3]]

Barplot by cluster family :

quantif = table(sobj$sample_identifier) %>%
  as.data.frame.table() %>%
  `colnames<-`(c("Sample", "nb_cells"))

aquarius::plot_barplot(df = table(sobj$sample_identifier,
                                  sobj$cluster_family) %>%
                         as.data.frame.table() %>%
                         `colnames<-`(c("Sample", "Cell Type", "Number")),
                       x = "Sample", y = "Number", fill = "Cell Type",
                       position = position_fill()) +
  ggplot2::geom_label(data = quantif, inherit.aes = FALSE,
                      aes(x = .data$Sample, y = 1.05, label = .data$nb_cells),
                      label.size = 0, size = 4) +
  ggplot2::scale_fill_manual(values = unlist(family_color),
                             breaks = names(family_color),
                             name = "Cell Family") +
  ggplot2::scale_y_continuous(breaks = seq(0, 1, by = 0.25),
                              labels = paste0(seq(0, 100, by = 25), sep = " %"),
                              expand = ggplot2::expansion(add = c(0, 0.05))) +
  ggplot2::theme(axis.title.y = element_blank(),
                 axis.line.x = element_line(colour = "lightgray"),
                 text = element_text(size = 15),
                 axis.text.x = element_text(margin = margin(t = 25, r = 0, b = 0, l = 0)),
                 legend.position = "none")

Heatmap of cluster proportion by sample :

group_by = "seurat_clusters"

cluster_by_sample = table(sobj$sample_identifier,
                          sobj@meta.data[, group_by]) %>%
  prop.table(margin = 1) %>%
  as.matrix()

## Right annotation : number of cells by dataset
ht_annot = table(sobj$sample_identifier) %>%
  as.data.frame.table() %>%
  `colnames<-`(c("sample_identifier", "nb_cells")) %>%
  `rownames<-`(.$sample_identifier) %>%
  dplyr::select(-sample_identifier)

ha_right = ComplexHeatmap::HeatmapAnnotation(
  df = ht_annot,
  which = "row",
  show_legend = TRUE,
  annotation_name_side = "top",
  col = list(nb_cells  = circlize::colorRamp2(colors = RColorBrewer::brewer.pal(name = "Greys", n = 9),
                                              breaks = seq(from = range(ht_annot$nb_cells)[1],
                                                           to = range(ht_annot$nb_cells)[2],
                                                           length.out = 9))))

## Left annotation : gender
ha_left = ComplexHeatmap::HeatmapAnnotation(
  gender = sample_info$gender,
  which = "row",
  show_legend = TRUE,
  annotation_name_side = "top",
  col = list(gender = setNames(nm = c("F", "M"),
                               c("lightcyan3", "navyblue"))))

## Top annotation : main cell type in this cluster
ht_annot = table(sobj$sample_identifier,
                 sobj@meta.data[, group_by]) %>%
  prop.table(margin = 1) %>%
  as.matrix()

ht_annot = table(sobj$cell_type,
                 sobj@meta.data[, group_by]) %>%
  prop.table(., margin = 2) %>%
  apply(., 2, which.max)
ht_annot = data.frame(row.names = names(ht_annot),
                      cell_type = names(color_markers)[ht_annot],
                      stringsAsFactors = FALSE)
ht_annot = dplyr::left_join(ht_annot, custom_order_cell_type, by = "cell_type") %>%
  # Simplification for matrix
  dplyr::mutate(cell_type = ifelse(cell_type %in% c("medulla", "cortex", "cuticle"), yes = "hair shaft", no = cell_type)) %>%
  # Simplification for T cells
  dplyr::mutate(cell_type = ifelse(cell_type %in% c("CD4 T cells", "CD8 T cells"), yes = "T cells", no = cell_type)) %>%
  # Simplification for APC
  dplyr::mutate(cell_type = ifelse(cell_type %in% c("Langerhans cells", "macrophages"), yes = "APC", no = cell_type)) %>%
  # Add color
  dplyr::mutate(color = as.character(color_markers[cell_type])) %>%
  dplyr::mutate(color = ifelse(cell_type == "hair shaft", yes = "#FFB6C1", no = color)) %>%
  dplyr::mutate(color = ifelse(cell_type == "T cells", yes = "#8A6EE6", no = color)) %>%
  dplyr::mutate(color = ifelse(cell_type == "APC", yes = "#9CAA4B", no = color))

ha_top = ComplexHeatmap::HeatmapAnnotation(
  # cell_type = ht_annot$cell_type,
  cell_family = ht_annot$cell_family,
  which = "column",
  show_legend = TRUE,
  show_annotation_name = FALSE,
  # annotation_name_side = "left",
  col = list(#cell_type = setNames(nm = ht_annot$cell_type,
    #                      ht_annot$color),
    cell_family = family_color
  ))

## Assemble heatmap
ht = ComplexHeatmap::Heatmap(cluster_by_sample,
                             heatmap_legend_param = list(title = "Proportion",
                                                         col = c("#2166AC", "#F7F7F7", "#B2182B")),
                             # bottom_annotation = ha_bottom,
                             right_annotation = ha_right,
                             left_annotation = ha_left,
                             top_annotation = ha_top,
                             cluster_rows = TRUE,
                             cluster_columns = TRUE,
                             row_title = "Sample",
                             row_names_gp = grid::gpar(names = sample_info$sample_identifier,
                                                       col = sample_info$color,
                                                       fontface = "bold"),
                             column_title = "Cluster",
                             column_names_centered = TRUE,
                             row_names_side = "left",
                             column_names_side = "top",
                             column_names_rot = 0)

## Draw !
ComplexHeatmap::draw(ht, merge_legends = TRUE)

For the dotplot, we clarify clusters and cell type annotation :

cell_type_in_cluster = table(sobj$cell_type, sobj$seurat_clusters) %>%
  prop.table(., margin = 1) %>%
  apply(., 1, which.max)
cell_type_in_cluster = cell_type_in_cluster - 1

missing_cluster = setdiff(levels(sobj$seurat_clusters),
                          cell_type_in_cluster)

cell_type_in_cluster = data.frame(cell_type = c(names(cell_type_in_cluster), cluster_type[missing_cluster]),
                                  cluster_id = c(cell_type_in_cluster, names(cluster_type[missing_cluster])),
                                  stringsAsFactors = FALSE, row.names = NULL) %>%
  dplyr::mutate(cluster_id = as.numeric(cluster_id)) %>%
  dplyr::arrange(cell_type, cluster_id)

custom_order_cell_type$clusters = custom_order_cell_type %>%
  apply(., MARGIN = 1, FUN = function(one_row) {
    cell_type = one_row["cell_type"]
    clusters = cell_type_in_cluster %>%
      dplyr::filter(.data$cell_type == .env$cell_type) %>%
      dplyr::pull(cluster_id)
    
    cell_type_cluster = paste0(cell_type, " (", paste0(clusters, collapse = ", "), ")")
    
    return(cell_type_cluster)
  }) %>%
  factor(., levels = .)

custom_order_cell_type
##                         cell_type  cell_family                     clusters
## CD4 T cells           CD4 T cells immune cells              CD4 T cells (2)
## CD8 T cells           CD8 T cells immune cells          CD8 T cells (2, 14)
## Langerhans cells Langerhans cells immune cells         Langerhans cells (6)
## macrophages           macrophages immune cells              macrophages (6)
## B cells                   B cells immune cells                 B cells (16)
## cuticle                   cuticle       matrix                  cuticle (3)
## cortex                     cortex       matrix                   cortex (3)
## medulla                   medulla       matrix                 medulla (11)
## IRS                           IRS       matrix                     IRS (15)
## proliferative       proliferative       matrix proliferative (4, 9, 10, 13)
## HFSC                         HFSC   non matrix                  HFSC (7, 8)
## ORS                           ORS   non matrix                      ORS (0)
## IBL                           IBL   non matrix                      IBL (1)
## IFE                           IFE   non matrix                      IFE (5)
## sebocytes               sebocytes   non matrix               sebocytes (12)

Dotplot :

plot_list = aquarius::plot_dotplot(sobj,
                                   markers = c("PTPRC",
                                               "CD3E", "CD4",
                                               "CD3E", "CD8A",
                                               "CD207", "AIF1",
                                               "TREM2", "MSR1",
                                               "CD79A", "CD79B",
                                               # "PRDM1", "KRT85",
                                               "MSX2",
                                               "KRT32", "KRT35",
                                               "KRT31", "PRR9",
                                               "BAMBI", "ALDH1A3",
                                               "KRT71", "KRT73",
                                               "TOP2A", "MCM5",
                                               "KRT14", "CXCL14",
                                               "KRT15", "COL17A1",
                                               "DIO2", "TCEAL2",
                                               "KRT16", "KRT6C",
                                               "SPINK5", "LY6D",
                                               "CLMP", "PPARG"),
                                   assay = "RNA", column_name = "cell_type", nb_hline = 0) +
  ggplot2::scale_color_gradientn(colors = aquarius::color_gene) +
  ggplot2::theme(legend.position = "left",
                 legend.justification = "bottom",
                 legend.box = "vertical",
                 legend.box.margin = margin(0,70,0,0),
                 axis.title = element_blank(),
                 axis.ticks.x = element_blank(),
                 axis.text.x = element_blank(),
                 axis.line.x = element_blank(),
                 plot.margin = unit(rep(0, 4), "cm"))

p = ggplot2::ggplot(custom_order_cell_type, aes(x = clusters, y = 0)) +
  ggplot2::geom_point(size = 0) +
  ggplot2::geom_segment(aes(x = 0.5, xend = 5.5, y = 0, yend = 0), size = 6, col = family_color["immune cells"]) +
  ggplot2::geom_segment(aes(x = 5.5, xend = 10.5, y = 0, yend = 0), size = 6, col = family_color["matrix"]) +
  ggplot2::geom_segment(aes(x = 10.5, xend = 15.5, y = 0, yend = 0), size = 6, col = family_color["non matrix"]) +
  ggplot2::scale_y_continuous(expand = c(0,0), limits = c(0,0)) +
  ggplot2::theme_classic() +
  ggplot2::theme(axis.text.y = element_blank(),
                 axis.ticks.y = element_blank(),
                 axis.title = element_blank(),
                 axis.line.y = element_blank(),
                 axis.text.x = element_text(angle = 45, hjust = 1, size = 10, color = "black"),
                 plot.margin = unit(c(0,0.5,0.5,0), "cm"))

plot_list = patchwork::wrap_plots(plot_list, p,
                                  ncol = 1, heights = c(25, 1))
plot_list

Immune cells

Settings

We load the immune cells dataset :

sobj_ic = readRDS(paste0(data_dir, "/4_zoom/1_zoom_immune/immune_cells_sobj.rds"))
sobj_ic
## An object of class Seurat 
## 15121 features across 2329 samples within 1 assay 
## Active assay: RNA (15121 features, 2000 variable features)
##  6 dimensional reductions calculated: RNA_pca, RNA_pca_20_tsne, RNA_pca_20_umap, harmony, harmony_20_umap, harmony_20_tsne

This is the projection name to visualize cells :

name2D = "harmony_20_tsne"

To represent results from differential expression, we load the analyses results :

list_results = readRDS(paste0(data_dir, "/4_zoom/1_zoom_immune/immune_cells_list_results.rds"))

lapply(list_results, FUN = names)
## $`Langerhans cells`
## [1] "mark" "gsea"
## 
## $macrophages
## [1] "mark"       "enrichr_hs" "enrichr_hd" "gsea"      
## 
## $`CD4 T cells`
## [1] "mark"       "enrichr_hs" "enrichr_hd" "gsea"      
## 
## $`CD8 T cells`
## [1] "mark"       "enrichr_hs" "enrichr_hd" "gsea"

Preparation

We defined cluster type and cluster family :

cluster_type = table(sobj_ic$cell_type, sobj_ic$seurat_clusters) %>%
  prop.table(., margin = 2) %>%
  apply(., 2, which.max)
cluster_type = setNames(nm = names(cluster_type),
                        levels(sobj_ic$cell_type)[cluster_type])

sobj_ic$cluster_type = cluster_type[sobj_ic$seurat_clusters]
sobj_ic$cluster_type = factor(sobj_ic$cluster_type,
                              levels = levels(sobj_ic$cell_type))
sobj_ic$cluster_family = custom_order_cell_type[sobj_ic$cluster_type, "cell_family"]
sobj_ic$cluster_family = factor(sobj_ic$cluster_family,
                                levels = names(family_color))

Figures

Control cells on the full atlas :

sobj$is_immune = (colnames(sobj) %in% colnames(sobj_ic))

Seurat::DimPlot(sobj, reduction = name2D_atlas, pt.size = 0.000001,
                group.by = "is_immune", order = "TRUE") +
  ggplot2::scale_color_manual(values = c(family_color[["immune cells"]], bg_color),
                              breaks = c(TRUE, FALSE)) +
  ggplot2::labs(title = "Immune cells",
                subtitle = paste0(ncol(sobj_ic), " cells")) +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5, face = "bold"),
                 plot.subtitle = element_text(hjust = 0.5)) +
  Seurat::NoAxes() + Seurat::NoLegend()

Violin plot of IL1B in macrophages :

subsobj = subset(sobj_ic, seurat_clusters == 2)
table(subsobj$sample_type)
## 
##  HS  HD 
## 378  31
il1b_hs = subsobj@assays$RNA@data["IL1B", subsobj$sample_type == "HS"]
il1b_hd = subsobj@assays$RNA@data["IL1B", subsobj$sample_type == "HD"]
il1b_hs_VS_il1b_hd = stats::t.test(il1b_hs, il1b_hd)
il1b_hs_VS_il1b_hd
## 
##  Welch Two Sample t-test
## 
## data:  il1b_hs and il1b_hd
## t = 2.3206, df = 35.801, p-value = 0.02612
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.05066074 0.75429252
## sample estimates:
## mean of x mean of y 
## 0.9256690 0.5231924
Seurat::VlnPlot(subsobj, group.by = "sample_type", pt.size = 0.3,
                features = "IL1B", cols = sample_type_colors) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")

Split by sample :

Seurat::VlnPlot(subsobj, group.by = "sample_identifier",
                features = "IL1B", cols = sample_info$color) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")

Violin plot of IL1B in macrophages :

il6_hs = subsobj@assays$RNA@data["IL6", subsobj$sample_type == "HS"]
il6_hd = subsobj@assays$RNA@data["IL6", subsobj$sample_type == "HD"]
il6_hs_VS_il6_hd = stats::t.test(il6_hs, il6_hd)
il6_hs_VS_il6_hd
## 
##  Welch Two Sample t-test
## 
## data:  il6_hs and il6_hd
## t = 2.3591, df = 377, p-value = 0.01883
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.001453521 0.016004661
## sample estimates:
##   mean of x   mean of y 
## 0.008729091 0.000000000
Seurat::VlnPlot(subsobj, group.by = "sample_type", pt.size = 0.3,
                features = "IL6", cols = sample_type_colors) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")

Violin plot of TNF in macrophages :

tnf_hs = subsobj@assays$RNA@data["TNF", subsobj$sample_type == "HS"]
tnf_hd = subsobj@assays$RNA@data["TNF", subsobj$sample_type == "HD"]
tnf_hs_VS_tnf_hd = stats::t.test(tnf_hs, tnf_hd)
tnf_hs_VS_tnf_hd
## 
##  Welch Two Sample t-test
## 
## data:  tnf_hs and tnf_hd
## t = 3.8395, df = 45.546, p-value = 0.0003786
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.1140962 0.3657054
## sample estimates:
##  mean of x  mean of y 
## 0.31784960 0.07794879
Seurat::VlnPlot(subsobj, group.by = "sample_type", pt.size = 0.3,
                features = "TNF", cols = sample_type_colors) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")

Split by sample :

Seurat::VlnPlot(subsobj, group.by = "sample_identifier",
                features = "TNF", cols = sample_info$color) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")

Violin plot of GZMA in CD4 T cells :

subsobj = subset(sobj_ic, seurat_clusters %in% c(0,10))
table(subsobj$sample_type)
## 
##  HS  HD 
## 569  90
gzma_hs = subsobj@assays$RNA@data["GZMA", subsobj$sample_type == "HS"]
gzma_hd = subsobj@assays$RNA@data["GZMA", subsobj$sample_type == "HD"]
gzma_hs_VS_gzma_hd = stats::t.test(gzma_hs, gzma_hd)
gzma_hs_VS_gzma_hd
## 
##  Welch Two Sample t-test
## 
## data:  gzma_hs and gzma_hd
## t = 14.755, df = 172.51, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  1.225578 1.604097
## sample estimates:
## mean of x mean of y 
## 1.8456108 0.4307735
Seurat::VlnPlot(subsobj, group.by = "sample_type", pt.size = 0.3,
                features = "GZMA", cols = sample_type_colors) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")

Violin plot of IFNG in CD4 T cells :

ifng_hs = subsobj@assays$RNA@data["IFNG", subsobj$sample_type == "HS"]
ifng_hd = subsobj@assays$RNA@data["IFNG", subsobj$sample_type == "HD"]
ifng_hs_VS_ifng_hd = stats::t.test(ifng_hs, ifng_hd)
ifng_hs_VS_ifng_hd
## 
##  Welch Two Sample t-test
## 
## data:  ifng_hs and ifng_hd
## t = 8.1978, df = 651.25, p-value = 1.303e-15
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.1874856 0.3055919
## sample estimates:
##  mean of x  mean of y 
## 0.25885178 0.01231299
Seurat::VlnPlot(subsobj, group.by = "sample_type", pt.size = 0.3,
                features = "IFNG", cols = sample_type_colors) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")

Violin plot of IL17A in CD4 T cells :

IL17_hs = subsobj@assays$RNA@data["IL17A", subsobj$sample_type == "HS"]
IL17_hd = subsobj@assays$RNA@data["IL17A", subsobj$sample_type == "HD"]
IL17_hs_VS_IL17_hd = stats::t.test(IL17_hs, IL17_hd)
IL17_hs_VS_IL17_hd
## 
##  Welch Two Sample t-test
## 
## data:  IL17_hs and IL17_hd
## t = 4.5667, df = 219.64, p-value = 8.255e-06
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.1412906 0.3558327
## sample estimates:
##  mean of x  mean of y 
## 0.30323619 0.05467451
Seurat::VlnPlot(subsobj, group.by = "sample_type", pt.size = 0.3,
                features = "IL17RE", cols = sample_type_colors) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")

We represent some genes split by sample type :

plot_list = lapply(c("IL1B", "GZMA", "IFNG", "IL17A", "TNF", "IL6"), FUN = function(one_gene) {
  p = aquarius::plot_split_dimred(sobj_ic,
                                  reduction = name2D,
                                  split_by = "sample_type",
                                  color_by = one_gene,
                                  color_palette = c("gray70", "#FDBB84", "#EF6548", "#7F0000", "black"),
                                  main_pt_size = 0.6,
                                  bg_pt_size = 0.6,
                                  order = TRUE,
                                  bg_color = "gray95")
  p = patchwork::wrap_plots(p, nrow = 1) +
    patchwork::plot_layout(guides = "collect") +
    ggplot2::theme(legend.position = "right") &
    ggplot2::theme(plot.subtitle = element_blank())
  return(p)
})

plot_list
## [[1]]

## 
## [[2]]

## 
## [[3]]

## 
## [[4]]

## 
## [[5]]

## 
## [[6]]

Barplot by cluster type :

quantif = table(sobj_ic$sample_identifier) %>%
  as.data.frame.table() %>%
  `colnames<-`(c("Sample", "nb_cells"))

aquarius::plot_barplot(df = table(sobj_ic$sample_identifier,
                                  sobj_ic$cluster_type) %>%
                         as.data.frame.table() %>%
                         `colnames<-`(c("Sample", "Cell Type", "Number")),
                       x = "Sample", y = "Number", fill = "Cell Type",
                       position = position_stack()) +
  ggplot2::geom_label(data = quantif, inherit.aes = FALSE,
                      aes(x = .data$Sample, y = 50 + .data$nb_cells, label = .data$nb_cells),
                      label.size = 0, size = 5) +
  ggplot2::scale_fill_manual(values = unlist(color_markers),
                             breaks = names(color_markers),
                             name = "Cell Type") +
  ggplot2::scale_y_continuous(limits = c(0, 100 + max(quantif$nb_cells)),
                              expand = ggplot2::expansion(add = c(0, 0.05))) +
  ggplot2::theme(axis.title.y = element_blank(),
                 axis.line.x = element_line(colour = "lightgray"),
                 text = element_text(size = 15),
                 legend.position = "none")

Heatmap for macrophages :

subsobj = subset(sobj_ic, cluster_type == "macrophages")
features_oi = c("IL1B", "TNF",
                "HLA-DQA2", "HLA-DPA1", "HLA-DRB5",
                "HLA-A", "HLA-C", "B2M",
                "C1QA", "C1QB", "C1QC")

# Matrix
mat_expr = Seurat::GetAssayData(subsobj)
mat_expr = mat_expr[features_oi, ]
mat_expr = Matrix::t(mat_expr)
mat_expr = dynutils::scale_quantile(mat_expr) # between 0 and 1
mat_expr = Matrix::t(mat_expr)
dim(mat_expr) # genes x cells
## [1]  11 463
## Colors
list_colors = list()

# Heatmap
list_colors[["expression"]] = rev(RColorBrewer::brewer.pal(name = "RdBu", n = 9))

# Sample annotation (top annotation)
list_colors[["sample_type"]] = sample_type_colors
list_colors[["sample_identifier"]] = setNames(nm = sample_info$sample_identifier,
                                              sample_info$color)
# Cells order
column_order = subsobj@meta.data %>%
  dplyr::arrange(sample_type, sample_identifier) %>%
  rownames()
column_order = match(column_order, rownames(subsobj@meta.data))

# Heatmap
ha_top = HeatmapAnnotation(sample_type = subsobj$sample_type,
                           sample_identifier = subsobj$sample_identifier,
                           col = list(sample_type = list_colors[["sample_type"]],
                                      sample_identifier = list_colors[["sample_identifier"]]))

# Heatmap
ht = Heatmap(as.matrix(mat_expr),
             heatmap_legend_param = list(title = "Expression", at = c(0, 1), 
                                         labels = c("low", "high")),
             col = list_colors[["expression"]],
             top_annotation = ha_top,
             show_column_names = FALSE,
             column_order = column_order,
             column_gap = unit(2, "mm"),
             cluster_rows = FALSE,
             row_title = NULL,
             row_names_gp = grid::gpar(fontsize = 14, fontface = "plain"),
             use_raster = FALSE,
             show_heatmap_legend = TRUE,
             border = TRUE)

ComplexHeatmap::draw(ht,
                     merge_legend = TRUE,
                     heatmap_legend_side = "bottom",
                     annotation_legend_side = "bottom")

Heatmap for CD4 T cells :

subsobj = subset(sobj_ic, cluster_type == "CD4 T cells")
features_oi = c("GZMA", "KLRB1", "BTG1", "ZFP36", "NFKBIA", "TXNIP", "CXCR4", "IFNG", "IL17A")

# Matrix
mat_expr = Seurat::GetAssayData(subsobj)
mat_expr = mat_expr[features_oi, ]
mat_expr = Matrix::t(mat_expr)
mat_expr = dynutils::scale_quantile(mat_expr) # between 0 and 1
mat_expr = Matrix::t(mat_expr)
dim(mat_expr) # genes x cells
## [1]   9 848
## Colors
list_colors = list()

# Heatmap
list_colors[["expression"]] = rev(RColorBrewer::brewer.pal(name = "RdBu", n = 9))

# Sample annotation (top annotation)
list_colors[["sample_type"]] = sample_type_colors
list_colors[["sample_identifier"]] = setNames(nm = sample_info$sample_identifier,
                                              sample_info$color)
# Cells order
column_order = subsobj@meta.data %>%
  dplyr::arrange(sample_type, sample_identifier) %>%
  rownames()
column_order = match(column_order, rownames(subsobj@meta.data))

# Heatmap
ha_top = HeatmapAnnotation(sample_type = subsobj$sample_type,
                           sample_identifier = subsobj$sample_identifier,
                           col = list(sample_type = list_colors[["sample_type"]],
                                      sample_identifier = list_colors[["sample_identifier"]]))

# Heatmap
ht = Heatmap(as.matrix(mat_expr),
             heatmap_legend_param = list(title = "Expression", at = c(0, 1), 
                                         labels = c("low", "high")),
             col = list_colors[["expression"]],
             top_annotation = ha_top,
             show_column_names = FALSE,
             column_order = column_order,
             column_gap = unit(2, "mm"),
             cluster_rows = FALSE,
             row_title = NULL,
             row_names_gp = grid::gpar(fontsize = 14, fontface = "plain"),
             use_raster = FALSE,
             show_heatmap_legend = TRUE,
             border = TRUE)

ComplexHeatmap::draw(ht,
                     merge_legend = TRUE,
                     heatmap_legend_side = "left",
                     annotation_legend_side = "left")

HFSC

Settings

We load the HFSCs dataset :

sobj_hfsc = readRDS(paste0(data_dir, "/4_zoom/2_zoom_hfsc/hfsc_sobj.rds"))
sobj_hfsc
## An object of class Seurat 
## 15384 features across 1454 samples within 1 assay 
## Active assay: RNA (15384 features, 2000 variable features)
##  6 dimensional reductions calculated: RNA_pca, RNA_pca_24_tsne, RNA_pca_24_umap, harmony, harmony_24_umap, harmony_24_tsne

This is the projection name to visualize cells :

name2D = "harmony_24_tsne"

To represent results from differential expression, we load the analyses results :

list_results = readRDS(paste0(data_dir, "/4_zoom/2_zoom_hfsc/hfsc_list_results.rds"))

lapply(list_results, FUN = names)
## $cluster_0_8
## [1] "p_val"     "avg_logFC" "pct.1"     "pct.2"     "p_val_adj"
## 
## $cluster_2
## [1] "p_val"     "avg_logFC" "pct.1"     "pct.2"     "p_val_adj"
## 
## $cluster_1
## [1] "p_val"     "avg_logFC" "pct.1"     "pct.2"     "p_val_adj"
## 
## $cluster_3
## [1] "p_val"     "avg_logFC" "pct.1"     "pct.2"     "p_val_adj"

Figures

HFSCs on the full atlas :

sobj$is_hfsc = (colnames(sobj) %in% colnames(sobj_hfsc))

Seurat::DimPlot(sobj, reduction = name2D_atlas, pt.size = 0.000001,
                group.by = "is_hfsc", order = "TRUE") +
  ggplot2::scale_color_manual(values = c(color_markers[["HFSC"]], bg_color),
                              breaks = c(TRUE, FALSE)) +
  ggplot2::labs(title = "HFSCs",
                subtitle = paste0(ncol(sobj_hfsc), " cells")) +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5, face = "bold"),
                 plot.subtitle = element_text(hjust = 0.5)) +
  Seurat::NoAxes() + Seurat::NoLegend()

KRT15 expression :

Seurat::FeaturePlot(sobj, reduction = name2D_atlas, pt.size = 0.000001,
                    features = "KRT15") +
  ggplot2::scale_color_gradientn(colors = aquarius::color_gene) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() + Seurat::NoLegend()

Genes of interest :

genes = c("TGFB2", "ANGPTL7", "FGF18", "MGP", "EPCAM", "KRT75", "NOTCH3", "PTHLH")

plot_list = lapply(c(1:length(genes)), FUN = function(gene_id) {
  gene = genes[[gene_id]]
  
  sobj_hfsc$my_gene = Seurat::FetchData(sobj_hfsc, gene)[, 1] %>%
    aquarius::run_rescale(., new_min = 0, new_max = 10)
  
  Seurat::FeaturePlot(sobj_hfsc, features = "my_gene", reduction = name2D) +
    ggplot2::scale_color_gradientn(colors = aquarius::color_gene,
                                   breaks = seq(0, 10, by = 2.5),
                                   labels = c("min", rep("", 3), "max")) +
    ggplot2::labs(title = gene) +
    ggplot2::theme(aspect.ratio = 1,
                   plot.title = element_text(hjust = 0.5, size = 17),
                   plot.subtitle = element_text(hjust = 0.5, size = 15),
                   legend.text = element_text(size = 15),
                   legend.position = "none") +
    Seurat::NoAxes()
})

plot_list
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## [[4]]

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Project name :

# Random order
set.seed(1234)
rnd_order = sample(colnames(sobj_hfsc), replace = FALSE, size = ncol(sobj_hfsc))

# Extract coordinates
cells_coord = sobj_hfsc@reductions[[name2D]]@cell.embeddings %>%
  as.data.frame() %>%
  `colnames<-`(c("Dim1", "Dim2"))
cells_coord$project_name = sobj_hfsc$project_name
cells_coord = cells_coord[(rnd_order), ]

# Plot
ggplot2::ggplot(cells_coord, aes(x = Dim1, y = Dim2, col = project_name)) +
  ggplot2::geom_point(size = 1.2) +
  ggplot2::scale_color_manual(values = sample_info$color,
                              breaks = sample_info$project_name) +
  ggplot2::theme_void() +
  ggplot2::theme(aspect.ratio = 1,
                 legend.position = "none")

Cluster :

Seurat::DimPlot(sobj_hfsc, reduction = name2D, pt.size = 1,
                group.by = "seurat_clusters", cols = grey_palette,
                label = TRUE, label.size = 7) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() +
  Seurat::NoLegend()

Heatmap with proportions :

cluster_markers = c("TGFB2", "ANGPTL7", "EPCAM", "KRT75", "NOTCH3", "PTHLH")

## Bottom annotation : gene expression by cluster
ht_annot = Seurat::FetchData(sobj_hfsc, slot = "data", vars = cluster_markers) %>%
  as.data.frame()
ht_annot$clusters = sobj_hfsc$seurat_clusters
ht_annot = ht_annot %>%
  dplyr::group_by(clusters) %>%
  dplyr::summarise_all(funs('mean' = mean)) %>%
  as.data.frame() %>%
  dplyr::select(-clusters) %>%
  `colnames<-`(c(cluster_markers))

ha_bottom = ComplexHeatmap::HeatmapAnnotation(df = ht_annot,
                                              which = "column",
                                              show_legend = TRUE,
                                              col = setNames(nm = cluster_markers,
                                                             lapply(cluster_markers, FUN = color_fun)),
                                              annotation_name_side = "left")

## Right annotation : number of cells by dataset
ht_annot = table(sobj_hfsc$sample_identifier) %>%
  as.data.frame.table() %>%
  `colnames<-`(c("sample_identifier", "nb_cells")) %>%
  `rownames<-`(.$sample_identifier) %>%
  dplyr::select(-sample_identifier)

ha_right = ComplexHeatmap::HeatmapAnnotation(
  df = ht_annot,
  which = "row",
  show_legend = TRUE,
  annotation_name_side = "bottom",
  col = list(nb_cells  = circlize::colorRamp2(colors = RColorBrewer::brewer.pal(name = "Greys", n = 9),
                                              breaks = seq(from = range(ht_annot$nb_cells)[1],
                                                           to = range(ht_annot$nb_cells)[2],
                                                           length.out = 9))))

## Heatmap
ht = aquarius::plot_prop_heatmap(df = sobj_hfsc@meta.data[, c("sample_identifier", "seurat_clusters")],
                                 bottom_annotation = ha_bottom,
                                 # right_annotation = ha_right,
                                 cluster_rows = TRUE,
                                 column_names_centered = TRUE,
                                 prop_margin = 1,
                                 row_names_gp = grid::gpar(names = sample_info$sample_identifier,
                                                           col = sample_info$color,
                                                           fontface = "bold"),
                                 row_title = "Sample",
                                 column_title = "Cluster")

ComplexHeatmap::draw(ht,
                     merge_legend = TRUE,
                     heatmap_legend_side = "bottom")

Heatmap for cluster 0 and 8 :

subsobj = subset(sobj_hfsc, seurat_clusters %in% c(0,8))

features_oi = rownames(list_results$cluster_0_8)
features_oi = features_oi[!grepl(features_oi, pattern = "^RP")]

# Matrix
mat_expr = Seurat::GetAssayData(subsobj)
mat_expr = mat_expr[features_oi, ]
mat_expr = Matrix::t(mat_expr)
mat_expr = cbind(mat_expr, subsobj$percent.mt)
colnames(mat_expr)[ncol(mat_expr)] = "percent.rb"
mat_expr = dynutils::scale_quantile(mat_expr) # between 0 and 1
mat_expr = Matrix::t(mat_expr)
dim(mat_expr) # genes x cells
## [1]  48 631
## Colors
list_colors = list()

# Heatmap
list_colors[["expression"]] = rev(RColorBrewer::brewer.pal(name = "RdBu", n = 9))

# Sample annotation (top annotation)
list_colors[["sample_type"]] = sample_type_colors
list_colors[["sample_identifier"]] = setNames(nm = sample_info$sample_identifier,
                                              sample_info$color)
# list_colors[["seurat_clusters"]] = setNames(aquarius::gg_color_hue(length(levels(subsobj$seurat_clusters))),
#                                             nm = levels(subsobj$seurat_clusters))
# Cells order
column_order = subsobj@meta.data %>%
  dplyr::arrange(sample_type, sample_identifier) %>%
  rownames()
column_order = match(column_order, rownames(subsobj@meta.data))

# Heatmap
ha_top = HeatmapAnnotation(sample_type = subsobj$sample_type,
                           sample_identifier = subsobj$sample_identifier,
                           # clusters = subsobj$seurat_clusters,
                           col = list(sample_type = list_colors[["sample_type"]],
                                      sample_identifier = list_colors[["sample_identifier"]]
                                      # clusters = list_colors[["seurat_clusters"]]
                           ))


# g1 : REACTOME_CYTOKINE_SIGNALING_IN_IMMUNE_SYSTEM
# g2 : GOBP_APOPTOTIC_PROCESS
g1_genes = c("B2M", "HLA-C", "HLA-A", "MIF", "PPIA", "JUNB", "IFITM3")
g2_genes = c("Jun", "ATF3", "BTG2", "RHOB", "NFKBIA", "SGK1", "KLF9",
             "CAV1", "DDIT4", "PDK4", "TXNIP", "RNF1152", "TLE1")
ha_right = data.frame(genes =  c(features_oi, "percent.rb"), rownames = c(features_oi, "percent.rb"))
ha_right$group = case_when(ha_right$genes %in% g1_genes ~ "REACTOME_CYTOKINE_SIGNALING_IN_IMMUNE_SYSTEM",
                           ha_right$genes %in% g2_genes ~ "GOBP_APOPTOTIC_PROCESS",
                           TRUE ~ "others")

list_colors[["group"]] = setNames(
  nm = c("REACTOME_CYTOKINE_SIGNALING_IN_IMMUNE_SYSTEM", "GOBP_APOPTOTIC_PROCESS", "others"),
  c("red", "black", "gray90"))

ha_right = HeatmapAnnotation(group = ha_right$group,
                             col = list(group = list_colors[["group"]]),
                             which = "row",
                             show_annotation_name = FALSE,
                             show_legend = TRUE)

# Heatmap
ht = Heatmap(as.matrix(mat_expr),
             heatmap_legend_param = list(title = "Expression", at = c(0, 1), 
                                         labels = c("low", "high")),
             col = list_colors[["expression"]],
             top_annotation = ha_top,
             right_annotation = ha_right,
             show_column_names = FALSE,
             column_order = column_order,
             column_gap = unit(2, "mm"),
             cluster_rows = FALSE,
             row_title = NULL,
             row_names_gp = grid::gpar(fontsize = 10, fontface = "plain"),
             use_raster = FALSE,
             show_heatmap_legend = TRUE,
             border = TRUE)

ComplexHeatmap::draw(ht,
                     merge_legend = TRUE,
                     heatmap_legend_side = "bottom",
                     annotation_legend_side = "bottom")

Violin plot of IFITM3 :

table(subsobj$sample_type)
## 
##  HS  HD 
## 588  43
ifitm3_hs = subsobj@assays$RNA@data["IFITM3", subsobj$sample_type == "HS"]
ifitm3_hd = subsobj@assays$RNA@data["IFITM3", subsobj$sample_type == "HD"]
ifitm3_hs_VS_ifitm3_hd = stats::t.test(ifitm3_hs, ifitm3_hd)
ifitm3_hs_VS_ifitm3_hd
## 
##  Welch Two Sample t-test
## 
## data:  ifitm3_hs and ifitm3_hd
## t = 7.0204, df = 47.675, p-value = 7.081e-09
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.5509680 0.9933324
## sample estimates:
## mean of x mean of y 
##  1.775647  1.003497
Seurat::VlnPlot(subsobj, group.by = "sample_type",
                features = "IFITM3", cols = sample_type_colors) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")

Split by sample :

Seurat::VlnPlot(subsobj, group.by = "sample_identifier",
                features = "IFITM3", cols = sample_info$color) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")

Violin plot of DDIT4 :

table(subsobj$sample_type)
## 
##  HS  HD 
## 588  43
DDIT4_hs = subsobj@assays$RNA@data["DDIT4", subsobj$sample_type == "HS"]
DDIT4_hd = subsobj@assays$RNA@data["DDIT4", subsobj$sample_type == "HD"]
DDIT4_hs_VS_DDIT4_hd = stats::t.test(DDIT4_hs, DDIT4_hd)
DDIT4_hs_VS_DDIT4_hd
## 
##  Welch Two Sample t-test
## 
## data:  DDIT4_hs and DDIT4_hd
## t = -11.338, df = 46.368, p-value = 5.774e-15
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -2.614197 -1.826081
## sample estimates:
## mean of x mean of y 
## 0.9376455 3.1577846
Seurat::VlnPlot(subsobj, group.by = "sample_type",
                features = "DDIT4", cols = sample_type_colors) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")

Split by sample :

Seurat::VlnPlot(subsobj, group.by = "sample_identifier",
                features = "DDIT4", cols = sample_info$color) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")

We represent some genes split by sample type :

plot_list = lapply(c("DDIT4", "IFITM3"), FUN = function(one_gene) {
  p = aquarius::plot_split_dimred(sobj_hfsc,
                                  reduction = name2D,
                                  split_by = "sample_type",
                                  color_by = one_gene,
                                  color_palette = c("gray70", "#FDBB84", "#EF6548", "#7F0000", "black"),
                                  main_pt_size = 0.6,
                                  bg_pt_size = 0.6,
                                  order = TRUE,
                                  bg_color = "gray95")
  p = patchwork::wrap_plots(p, nrow = 1) +
    patchwork::plot_layout(guides = "collect") +
    ggplot2::theme(legend.position = "right") &
    ggplot2::theme(plot.subtitle = element_blank())
  return(p)
})

patchwork::wrap_plots(plot_list, ncol = 2)

Barplot with number of HFSCs and total number of cells :

quantif = dplyr::left_join(
  x = table(sobj$sample_identifier) %>%
    as.data.frame.table() %>%
    `colnames<-`(c("Sample", "nb_cells")),
  y = table(sobj_hfsc$sample_identifier) %>%
    as.data.frame.table() %>%
    `colnames<-`(c("Sample", "nb_hfsc")),
  by = "Sample") %>%
  dplyr::mutate(prop_hfsc = round(100*nb_hfsc / nb_cells, 2))

quantif_to_plot = rbind.data.frame(
  data.frame(Sample = quantif$Sample,
             nb_cells = quantif$nb_cells - quantif$nb_hfsc,
             cell_type = "others",
             stringsAsFactors = FALSE),
  data.frame(Sample = quantif$Sample,
             nb_cells = quantif$nb_hfsc,
             cell_type = "hfsc",
             stringsAsFactors = FALSE)) %>%
  dplyr::mutate(cell_type = factor(cell_type, levels = c("others", "hfsc")))

aquarius::plot_barplot(df = quantif_to_plot,
                       x = "Sample", y = "nb_cells", fill = "cell_type",
                       position = position_fill()) +
  ggplot2::geom_label(data = quantif, inherit.aes = FALSE,
                      aes(x = .data$Sample, y = 0.05+.data$prop_hfsc/100, label = .data$nb_hfsc),
                      label.size = 0, size = 5, fill = NA) +
  ggplot2::scale_fill_manual(values = c("gray90", color_markers[["HFSC"]]),
                             breaks = c("others", "hfsc"),
                             name = "Cell Type") +
  ggplot2::scale_y_continuous(breaks = seq(0, 1, by = 0.25),
                              labels = paste0(seq(0, 100, by = 25), sep = " %"),
                              expand = ggplot2::expansion(add = c(0, 0.05))) +
  ggplot2::theme(axis.title.y = element_blank(),
                 axis.line.x = element_line(colour = "lightgray"),
                 text = element_text(size = 15),
                 legend.position = "none")

IBL and ORS

Settings

We load the IBL + ORS dataset :

sobj_iblors = readRDS(paste0(data_dir, "/4_zoom/3_zoom_iblmors/iblmors_sobj.rds"))
sobj_iblors
## An object of class Seurat 
## 16701 features across 3532 samples within 1 assay 
## Active assay: RNA (16701 features, 2000 variable features)
##  6 dimensional reductions calculated: RNA_pca, RNA_pca_20_tsne, RNA_pca_20_umap, harmony, harmony_20_umap, harmony_20_tsne

This is the projection name to visualize cells :

name2D = "harmony_20_tsne"

To represent results from differential expression, we load the analyses results :

list_results = readRDS(paste0(data_dir, "/4_zoom/3_zoom_iblmors/iblmors_list_results.rds"))

lapply(list_results, FUN = names)
## $IBL_vs_ORS
## [1] "mark"        "enrichr_ibl" "enrichr_ors" "gsea"       
## 
## $cluster5_vs_ORS
## [1] "mark"       "enrichr_up" "enrichr_dn" "gsea"      
## 
## $IBL_HS_vs_HD
## [1] "mark"       "enrichr_hs" "enrichr_hd" "gsea"      
## 
## $ORS_HS_vs_HD
## [1] "mark"       "enrichr_hs" "enrichr_hd" "gsea"

Preparation

We defined cluster type :

cluster_type = table(sobj_iblors$cell_type, sobj_iblors$seurat_clusters) %>%
  prop.table(., margin = 2) %>%
  apply(., 2, which.max)
cluster_type = setNames(nm = names(cluster_type),
                        levels(sobj_iblors$cell_type)[cluster_type])

sobj_iblors$cluster_type = cluster_type[sobj_iblors$seurat_clusters]
sobj_iblors$cluster_type = factor(sobj_iblors$cluster_type,
                                  levels = c("IBL", "ORS"))

Figures

IBL + ORS on the full atlas :

sobj$cell_bc = colnames(sobj)
sobj_iblors$cell_bc = colnames(sobj_iblors)
sobj$is_iblors = dplyr::left_join(sobj@meta.data[, c("cell_bc", "percent.mt")],
                                  sobj_iblors@meta.data[, c("cell_bc", "cluster_type")],
                                  by = "cell_bc")[, "cluster_type"]

Seurat::DimPlot(sobj, reduction = name2D_atlas, pt.size = 0.000001,
                group.by = "is_iblors", order = "TRUE") +
  ggplot2::scale_color_manual(values = c(color_markers[c("IBL", "ORS")], bg_color),
                              breaks = c("IBL", "ORS", NA), na.value = bg_color) +
  ggplot2::labs(title = "IBL + ORS",
                subtitle = paste0(ncol(sobj_iblors), " cells")) +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5, face = "bold"),
                 plot.subtitle = element_text(hjust = 0.5)) +
  Seurat::NoAxes() + Seurat::NoLegend()

Project name :

# Random order
set.seed(1234)
rnd_order = sample(colnames(sobj_iblors), replace = FALSE, size = ncol(sobj_iblors))

# Extract coordinates
cells_coord = sobj_iblors@reductions[[name2D]]@cell.embeddings %>%
  as.data.frame() %>%
  `colnames<-`(c("Dim1", "Dim2"))
cells_coord$project_name = sobj_iblors$project_name
cells_coord = cells_coord[(rnd_order), ]

# Plot
ggplot2::ggplot(cells_coord, aes(x = Dim1, y = Dim2, col = project_name)) +
  ggplot2::geom_point(size = 1.2) +
  ggplot2::scale_color_manual(values = sample_info$color,
                              breaks = sample_info$project_name) +
  ggplot2::theme_void() +
  ggplot2::theme(aspect.ratio = 1,
                 legend.position = "none")

Cluster :

Seurat::DimPlot(sobj_iblors, reduction = name2D, pt.size = 1,
                group.by = "seurat_clusters", cols = grey_palette,
                label = TRUE, label.size = 8) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() +
  Seurat::NoLegend()

Cluster type :

Seurat::DimPlot(sobj_iblors, reduction = name2D, pt.size = 1,
                group.by = "cluster_type", cols = color_markers) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() +
  Seurat::NoLegend()

Cluster type split by sample type :

plot_list = aquarius::plot_split_dimred(sobj_iblors, reduction = name2D,
                                        group_by = "cluster_type",
                                        group_color = color_markers,
                                        split_by = "sample_type",
                                        bg_pt_size = 1, main_pt_size = 1,
                                        bg_color = bg_color)

patchwork::wrap_plots(plot_list) &
  Seurat::NoLegend()

Barplot by cluster family :

sobj_iblors$cluster_type_sep5 = ifelse(sobj_iblors$seurat_clusters == 5,
                                       yes = "ORS_5",
                                       no = as.character(sobj_iblors$cluster_type)) %>%
  as.factor()

quantif = table(sobj_iblors$sample_identifier) %>%
  as.data.frame.table() %>%
  `colnames<-`(c("Sample", "nb_cells"))

quantif_to_plot = table(sobj_iblors$sample_identifier,
                        sobj_iblors$cluster_type_sep5) %>%
  as.data.frame.table() %>%
  `colnames<-`(c("Sample", "CellType", "Number")) %>%
  dplyr::mutate(Style = ifelse(CellType == "ORS_5", yes = "IL1R2+", no = "IL1R2-")) %>%
  dplyr::mutate(Style = factor(Style, levels = c("IL1R2-", "IL1R2+"))) %>%
  dplyr::mutate(CellType = ifelse(CellType == "ORS_5", yes = "ORS", no = as.character(CellType))) %>%
  `colnames<-`(c("Sample", "Cell Type", "Number", "IL1R2 status"))

aquarius::plot_barplot(df = quantif_to_plot,
                       x = "Sample", y = "Number",
                       fill = "Cell Type", pattern = "IL1R2 status",
                       position = position_fill()) +
  ggplot2::geom_label(data = quantif, inherit.aes = FALSE,
                      aes(x = .data$Sample, y = 1.05, label = .data$nb_cells),
                      label.size = 0, size = 5) +
  ggplot2::scale_fill_manual(values = unlist(color_markers[levels(sobj_iblors$cluster_type)]),
                             breaks = names(color_markers[levels(sobj_iblors$cluster_type)]),
                             name = "Cell Type") +
  ggplot2::scale_y_continuous(breaks = seq(0, 1, by = 0.25),
                              labels = paste0(seq(0, 100, by = 25), sep = " %"),
                              expand = ggplot2::expansion(add = c(0, 0.05))) +
  ggplot2::theme(axis.title.y = element_blank(),
                 axis.line.x = element_line(colour = "lightgray"),
                 text = element_text(size = 15),
                 legend.position = "right")

DE genes between IBL and ORS :

mark = list_results$IBL_vs_ORS$mark
mark$gene_name = rownames(mark)
mark_label = rbind(
  # up-regulated in IBL
  mark %>% dplyr::top_n(., n = 20, wt = avg_logFC),
  # up-regulated in ORS
  mark %>% dplyr::top_n(., n = 20, wt = -avg_logFC),
  # representative and selective for IBL
  mark %>% dplyr::top_n(., n = 20, wt = (pct.1 - pct.2)),
  # representative and selective for ORS
  mark %>% dplyr::top_n(., n = 20, wt = -(pct.1 - pct.2))) %>%
  dplyr::distinct()
mark_label = mark_label[!grepl(rownames(mark_label), pattern = "^MT"), ]

avg_logFC_range = setNames(c(min(mark_label$avg_logFC), -1, 0, 1, max(mark_label$avg_logFC)),
                           nm = c("dodgerblue4", "dodgerblue3", "#B7B7B7", "firebrick3", "firebrick4"))


ggplot2::ggplot(mark, aes(x = pct.1, y = pct.2, col = avg_logFC)) +
  ggplot2::geom_abline(slope = 1, intercept = 0, lty = 2) +
  ggplot2::geom_point() +
  ggrepel::geom_label_repel(data = mark_label, max.overlaps = Inf,
                            aes(x = pct.1, y = pct.2, label = gene_name),
                            col = "black", fill = NA, size = 3.5, label.size = NA) +
  ggplot2::labs(x = "Enriched in IBL",
                y = "Enriched in ORS") +
  ggplot2::scale_color_gradientn(colors = names(avg_logFC_range),
                                 values = scales::rescale(unname(avg_logFC_range))) +
  ggplot2::theme_classic() +
  ggplot2::theme(aspect.ratio = 1)

GSEA plot :

the_gs_name = "REACTOME_KERATINIZATION" 
the_content = list_results$IBL_vs_ORS$gsea@result %>%
  dplyr::filter(ID == the_gs_name)
the_subtitle = paste0("\nNES : ", round(the_content$NES, 2),
                      " | pvalue : ", round(the_content$pvalue, 4),
                      " | set size : ", the_content$setSize, " genes")

enrichplot::gseaplot2(x = list_results$IBL_vs_ORS$gsea,
                      geneSetID = the_gs_name) +
  ggplot2::labs(title = the_gs_name,
                subtitle = the_subtitle) +
  ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold",
                                           margin = ggplot2::margin(3, 3, 5, 3)),
                 plot.subtitle = ggtext::element_markdown(hjust = 0.5,
                                                          size = 10))

the_gs_name = "HALLMARK_INTERFERON_GAMMA_RESPONSE" 
the_content = list_results$IBL_vs_ORS$gsea@result %>%
  dplyr::filter(ID == the_gs_name)
the_subtitle = paste0("\nNES : ", round(the_content$NES, 2),
                      " | pvalue : ", round(the_content$pvalue, 4),
                      " | set size : ", the_content$setSize, " genes")

enrichplot::gseaplot2(x = list_results$IBL_vs_ORS$gsea,
                      geneSetID = the_gs_name) +
  ggplot2::labs(title = the_gs_name,
                subtitle = the_subtitle) +
  ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold",
                                           margin = ggplot2::margin(3, 3, 5, 3)),
                 plot.subtitle = ggtext::element_markdown(hjust = 0.5,
                                                          size = 10))

Score for both gene sets, in all cells :

gene_sets = aquarius::get_gene_sets(species = "Homo sapiens")
the_gs_name = "REACTOME_KERATINIZATION" 
the_gs_content = gene_sets$gene_sets_full %>%
  dplyr::filter(gs_name == the_gs_name) %>%
  dplyr::pull(gene_symbol) %>%
  unlist()

sobj_iblors$score_kera = Seurat::AddModuleScore(sobj_iblors,
                                                features = list(the_gs_content))$Cluster1

Seurat::VlnPlot(sobj_iblors, features = "score_kera", pt.size = 0.05,
                split.by = "sample_type", group.by = "cluster_type",
                cols = rev(sample_type_colors)) +
  ggplot2::labs(title = the_gs_name) +
  ggplot2::theme(axis.title.x = element_blank())

the_gs_name = "HALLMARK_INTERFERON_GAMMA_RESPONSE" 
the_gs_content = gene_sets$gene_sets_full %>%
  dplyr::filter(gs_name == the_gs_name) %>%
  dplyr::pull(gene_symbol) %>%
  unlist()

sobj_iblors$score_ifna = Seurat::AddModuleScore(sobj_iblors,
                                                features = list(the_gs_content))$Cluster1

Seurat::VlnPlot(sobj_iblors, features = "score_ifna", pt.size = 0.05,
                split.by = "sample_type", group.by = "cluster_type",
                cols = rev(sample_type_colors)) +
  ggplot2::labs(title = the_gs_name) +
  ggplot2::theme(axis.title.x = element_blank())

Violin plot for IBL :

subsobj = subset(sobj_iblors, cluster_type == "IBL")

Seurat::VlnPlot(subsobj, group.by = "sample_type", pt.size = 0.05,
                features = c("DUSP1", "DDIT4", "MIF", "LGALS7", "ARF5", "S100A9"),
                cols = sample_type_colors, ncol = 6) &
  ggplot2::theme(axis.title.x = element_blank(),
                 axis.title.y = element_blank(),
                 legend.position = "none")

Violin plot for ORS :

subsobj = subset(sobj_iblors, cluster_type == "ORS")

Seurat::VlnPlot(subsobj, group.by = "sample_type", pt.size = 0.05,
                features = c("DUSP1", "KLF6", "CLDN1", "CTGF",
                             "S100A9", "CCL2", "IFITM3", "IFI27"),
                cols = sample_type_colors, ncol = 8) &
  ggplot2::theme(axis.title.x = element_blank(),
                 axis.title.y = element_blank(),
                 legend.position = "none")

Split by sample :

Seurat::VlnPlot(subsobj, group.by = "sample_identifier",
                features = "IFI27", cols = sample_info$color) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")

Heatmap for cluster 5 vs other ORS :

subsobj = subset(sobj_iblors, cluster_type == "ORS")

features_oi = c("YBX3", "TXNIP", "KRT14", "KRT15", "NEAT1",
                "FXYD3", "MT2A", "MT1E", "MT1X", "AQP3", "GLUL",
                # "HALLMARK_TNFA_SIGNALING_VIA_NFKB"
                "FOS", "JUNB", "DUSP1", "ZFP36", "NFKBIZ",
                "ATF3", "RHOB",  "ETS2", "IL18", "KLF4", "KLF6", "KLF9",
                "KLF3", "KLF5", "COL17A1", "THSD4", "WNT3", "WNT4", "SLPI", "PLAT",
                "LAMB4", "DCN", "SPINK5",
                "GSTM3", "ALDH3A1",  "LGALS7B", "SLC38A2", "EHF",  "CLEC2B",
                "IL20RB", "IL1R2", "IFI27", "CXCL14", "HLA-C", "GPSM2", "DAAM1",   "ID1",
                "RNASET2", "HOPX", "POU3F1", "SPRY1", "AR", "PDGFC",
                "WFDC2", "WFDC5", "TSC22D3", "FGFR3",  "LY6D", "IGFBP3", 
                # Other ORS
                "APOE", "CTSB", "CALD1", "SOX4",
                "STMN1", "LMO4", "CEBPB", "TMEM45A", "GPX2", "C1QTNF12", "GJB6",
                "KRT6A", "KRT17", "RBP1", "CALML3", "PTN", "DAPK2",
                "EGLN3", "FILIP1L", "ADGRL3", "FST", "EFNB2", "SEMA5A",
                "FGFR1", "EGR2", "CLDN1", "DEFB1", "CARD18", "MGST1")

# Matrix
mat_expr = Seurat::GetAssayData(subsobj)
mat_expr = mat_expr[features_oi, ]
mat_expr = Matrix::t(mat_expr)
mat_expr = dynutils::scale_quantile(mat_expr) # between 0 and 1
mat_expr = Matrix::t(mat_expr)
dim(mat_expr) # genes x cells
## [1]   89 2022
## Colors
list_colors = list()
list_colors[["expression"]] = rev(RColorBrewer::brewer.pal(name = "RdBu", n = 9))
list_colors[["sample_type"]] = sample_type_colors
list_colors[["sample_identifier"]] = setNames(nm = sample_info$sample_identifier,
                                              sample_info$color)
list_colors[["population"]] = setNames(nm = c("IL1R2+ ORS", "other ORS"),
                                       c("black", color_markers["ORS"]))
list_colors[["nFeature_RNA"]] = circlize::colorRamp2(breaks = seq(from = min(subsobj$nFeature_RNA),
                                                                  to = max(subsobj$nFeature_RNA),
                                                                  length.out = 9),
                                                     colors = RColorBrewer::brewer.pal(name = "Greys", n = 9))

# Cells order
column_order = subsobj@meta.data %>%
  dplyr::mutate(seurat_clusters = factor(seurat_clusters, levels = c(5, 3, 0, 1, 7))) %>%
  dplyr::arrange(sample_type, seurat_clusters, sample_identifier) %>%
  rownames()
column_order = match(column_order, rownames(subsobj@meta.data))

# Annotation
ha_top = HeatmapAnnotation(sample_type = subsobj$sample_type,
                           sample_identifier = subsobj$sample_identifier,
                           population = ifelse(subsobj$cluster_type_sep5 == "ORS",
                                               yes = "other ORS", no = "IL1R2+ ORS"),
                           col = list(sample_type = list_colors[["sample_type"]],
                                      sample_identifier = list_colors[["sample_identifier"]],
                                      population = list_colors[["population"]]))

ha_bottom = HeatmapAnnotation(nFeature_RNA = subsobj$nFeature_RNA,
                              col = list(nFeature_RNA = list_colors[["nFeature_RNA"]]))

# Heatmap
ht = Heatmap(as.matrix(mat_expr),
             heatmap_legend_param = list(title = "Expression", at = c(0, 1), 
                                         labels = c("low", "high")),
             col = list_colors[["expression"]],
             top_annotation = ha_top,
             bottom_annotation = ha_bottom,
             # Cell grouping
             column_split = subsobj$sample_type %>% as.character(),
             cluster_columns = FALSE,
             column_order = column_order,
             column_title = NULL,
             show_column_dend = FALSE,
             show_column_names = FALSE,
             # Genes
             cluster_rows = FALSE,
             row_names_gp = grid::gpar(fontsize = 14, fontface = "plain"),
             # Style
             use_raster = FALSE,
             show_heatmap_legend = TRUE,
             border = TRUE)

ComplexHeatmap::draw(ht,
                     merge_legend = TRUE,
                     heatmap_legend_side = "right",
                     annotation_legend_side = "right")

Genes of interest :

genes = c("KRT16", "COL17A1", "DST", "KRT6B", "IL1R2", "WNT3",
          "IFI27", "CXCL14", "IGFBP3", "KRT15", "CD200")

plot_list = lapply(c(1:length(genes)), FUN = function(gene_id) {
  gene = genes[[gene_id]]
  
  sobj_iblors$my_gene = Seurat::FetchData(sobj_iblors, gene)[, 1] %>%
    aquarius::run_rescale(., new_min = 0, new_max = 10)
  
  Seurat::FeaturePlot(sobj_iblors, features = "my_gene", reduction = name2D, pt.size = 0.25) +
    ggplot2::scale_color_gradientn(colors = aquarius::color_gene,
                                   breaks = seq(0, 10, by = 2.5),
                                   labels = c("min", rep("", 3), "max")) +
    ggplot2::labs(title = gene) +
    ggplot2::theme(aspect.ratio = 1,
                   plot.title = element_text(hjust = 0.5, size = 17),
                   plot.subtitle = element_text(hjust = 0.5, size = 15),
                   legend.text = element_text(size = 15),
                   legend.position = "none") +
    Seurat::NoAxes()
})

plot_list
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HFSCs to IBL and ORS

Settings

We load the merged dataset :

sobj_traj = readRDS(paste0(data_dir, "/4_zoom/4_zoom_hfsc_iblmors/hfsc_iblmors_sobj_traj_tinga.rds"))
sobj_traj
## An object of class Seurat 
## 17050 features across 4986 samples within 1 assay 
## Active assay: RNA (17050 features, 2000 variable features)
##  8 dimensional reductions calculated: RNA_pca, RNA_pca_18_tsne, RNA_pca_18_umap, harmony, harmony_18_umap, harmony_18_tsne, harmony_dm, harmony_dm_5_umap

This is the projection name to visualize cells :

name2D = "harmony_dm"

We load the trajectory object for visualisation purpose :

my_traj = readRDS(paste0(data_dir, "/4_zoom/4_zoom_hfsc_iblmors/hfsc_iblmors_my_traj_tinga.rds"))
class(my_traj)
## [1] "dynwrap::with_dimred"     "dynwrap::with_trajectory"
## [3] "dynwrap::data_wrapper"    "list"

Preparation

We defined cell type based on individual object :

sobj_iblors$cell_bc = colnames(sobj_iblors)
sobj_traj$cell_bc = colnames(sobj_traj)
sobj_traj$cluster_type = dplyr::left_join(sobj_traj@meta.data[, c("cell_bc", "percent.mt")],
                                          sobj_iblors@meta.data[, c("cell_bc", "cluster_type")],
                                          by = "cell_bc")[, "cluster_type"] %>% as.character()
sobj_traj$cluster_type = ifelse(colnames(sobj_traj) %in% colnames(sobj_hfsc),
                                yes = "HFSC",
                                no = sobj_traj$cluster_type) %>%
  as.factor()

Figures

Cells on the full atlas :

sobj$cell_bc = colnames(sobj)
sobj_traj$cell_bc = colnames(sobj_traj)
sobj$is_traj = dplyr::left_join(sobj@meta.data[, c("cell_bc", "percent.mt")],
                                sobj_traj@meta.data[, c("cell_bc", "cluster_type")],
                                by = "cell_bc")[, "cluster_type"]

Seurat::DimPlot(sobj, reduction = name2D_atlas, pt.size = 0.000001,
                group.by = "is_traj", order = levels(sobj_traj$cluster_type)) +
  ggplot2::scale_color_manual(values = c(color_markers[c("IBL", "ORS", "HFSC")], bg_color),
                              breaks = c("IBL", "ORS", "HFSC", NA), na.value = bg_color) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() + Seurat::NoLegend()

Project name :

# Random order
set.seed(1234)
rnd_order = sample(colnames(sobj_traj), replace = FALSE, size = ncol(sobj_traj))

# Extract coordinates
cells_coord = sobj_traj@reductions[[name2D]]@cell.embeddings %>%
  as.data.frame() %>%
  `colnames<-`(c("Dim1", "Dim2"))
cells_coord$sample_type = sobj_traj$sample_type
cells_coord = cells_coord[order(sobj_traj$sample_type), ]

# Plot
ggplot2::ggplot(cells_coord, aes(x = Dim1, y = Dim2, col = sample_type)) +
  ggplot2::geom_point(size = 1.2) +
  ggplot2::scale_color_manual(values = sample_type_colors,
                              breaks = names(sample_type_colors)) +
  ggplot2::theme_void() +
  ggplot2::theme(aspect.ratio = 1,
                 legend.position = "none")

Cluster type :

Seurat::DimPlot(sobj_traj, reduction = name2D, pt.size = 0.5,
                group.by = "cluster_type", cols = color_markers) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() +
  Seurat::NoLegend()

Pseudotime :

Seurat::FeaturePlot(sobj_traj, reduction = name2D, pt.size = 0.5,
                    features = "pseudotime") +
  ggplot2::scale_color_gradientn(colors = viridis::viridis(n = 100)) +
  ggplot2::lims(x = range(sobj_traj@reductions[[name2D]]@cell.embeddings[, 1]),
                y = range(sobj_traj@reductions[[name2D]]@cell.embeddings[, 2])) +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_blank()) +
  Seurat::NoAxes()

Pseudotime with dynplot’s function :

dynplot::plot_dimred(trajectory = my_traj,
                     dimred = sobj_traj[[name2D]]@cell.embeddings,
                     # Cells
                     color_cells = 'pseudotime',
                     size_cells = 1.6,
                     border_radius_percentage = 0,
                     # Trajectory
                     plot_trajectory = TRUE,
                     color_trajectory = "none",
                     label_milestones = FALSE,
                     size_milestones = 0,
                     size_transitions = 1)

OEP002321 dataset

Settings

We load the dataset containing all cells :

sobj = readRDS(paste0(data_dir, "/5_wu/3_combined/wu_sobj.rds"))
sobj
## An object of class Seurat 
## 17727 features across 17762 samples within 1 assay 
## Active assay: RNA (17727 features, 2000 variable features)
##  6 dimensional reductions calculated: RNA_pca, RNA_pca_38_tsne, RNA_pca_38_umap, harmony, harmony_38_umap, harmony_38_tsne

This is the projection name to visualize cells :

name2D = "harmony_38_tsne"
name2D_atlas = name2D

These are all the samples analyzed :

sample_info = readRDS(paste0(data_dir, "/5_wu/1_metadata/wu_sample_info.rds"))

# Nb cells by dataset
to_plot = table(sobj$sample_identifier) %>%
  as.data.frame.table(., stringsAsFactors = FALSE) %>%
  `colnames<-`(c("sample_identifier", "nb_cells")) %>%
  dplyr::left_join(x = ., y = sample_info, by = "sample_identifier") 

# patchwork
plot_list = aquarius::fig_plot_gb(to_plot, title = "Available datasets")
patchwork::wrap_plots(plot_list) +
  patchwork::plot_layout(design = "A\nB", heights = c(0.1,5)) &
  ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold", size = 15))

We define cluster type and cluster family :

sobj$cell_type = sobj$cell_type %>%
  as.character() %>%
  factor(., levels = names(color_markers))

cluster_type = table(sobj$cell_type, sobj$seurat_clusters) %>%
  prop.table(., margin = 2) %>%
  apply(., 2, which.max)
cluster_type = setNames(nm = names(cluster_type),
                        levels(sobj$cell_type)[cluster_type])

sobj$cluster_type = cluster_type[sobj$seurat_clusters]
sobj$cluster_type = factor(sobj$cluster_type,
                           levels = levels(sobj$cell_type)) %>%
  base::droplevels()
sobj$cluster_family = custom_order_cell_type[sobj$cluster_type, "cell_family"]
sobj$cluster_family = factor(sobj$cluster_family,
                             levels = names(family_color))

Global figures

Project name :

# Random order
set.seed(1234)
rnd_order = sample(colnames(sobj), replace = FALSE, size = ncol(sobj))

# Extract coordinates
cells_coord = sobj@reductions[[name2D_atlas]]@cell.embeddings %>%
  as.data.frame() %>%
  `colnames<-`(c("Dim1", "Dim2"))
cells_coord$project_name = sobj$project_name
cells_coord = cells_coord[(rnd_order), ]

# Plot
ggplot2::ggplot(cells_coord, aes(x = Dim1, y = Dim2, col = project_name)) +
  ggplot2::geom_point(size = 0.5) +
  ggplot2::scale_color_manual(values = sample_info$color,
                              breaks = sample_info$project_name) +
  ggplot2::theme_void() +
  ggplot2::theme(aspect.ratio = 1,
                 legend.position = "none")

Cell type annotation :

Seurat::DimPlot(sobj, reduction = name2D, pt.size = 0.5,
                group.by = "cell_type", cols = color_markers) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() +
  Seurat::NoLegend()

Cluster type annotation :

Seurat::DimPlot(sobj, reduction = name2D, pt.size = 0.5,
                group.by = "cluster_type", cols = color_markers) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() +
  Seurat::NoLegend()

Gene expression to assess annotation :

genes = c("PTPRC", "MSX2", "KRT14")
names(genes) = c("immune cells", "matrix cells", "non-matrix cells")

plot_list = lapply(c(1:length(genes)), FUN = function(gene_id) {
  gene = genes[[gene_id]]
  pop = names(genes)[gene_id]
  
  sobj$my_gene = Seurat::FetchData(sobj, gene)[, 1] %>%
    aquarius::run_rescale(., new_min = 0, new_max = 10)
  
  Seurat::FeaturePlot(sobj, features = "my_gene", reduction = name2D_atlas) +
    ggplot2::scale_color_gradientn(colors = aquarius::color_gene,
                                   breaks = seq(0, 10, by = 2.5),
                                   labels = c("min", rep("", 3), "max")) +
    ggplot2::labs(title = gene) + 
    # subtitle = pop) +
    ggplot2::theme(aspect.ratio = 1,
                   plot.title = element_text(hjust = 0.5, size = 17),
                   plot.subtitle = element_text(hjust = 0.5, size = 15),
                   legend.text = element_text(size = 15),
                   legend.position = "none") +
    Seurat::NoAxes()
})

plot_list
## [[1]]

## 
## [[2]]

## 
## [[3]]

Dotplot :

custom_order_cell_type = custom_order_cell_type[levels(sobj$cluster_type), c("cell_type", "cell_family")]

plot_list = Seurat::DotPlot(sobj,
                            features = c("PTPRC",
                                         "CD3E", "CD4",
                                         "CD207", "AIF1",
                                         # "PRDM1", "KRT85",
                                         "MSX2",
                                         "KRT32", "KRT35",
                                         "KRT31", "PRR9",
                                         "BAMBI", "ALDH1A3",
                                         "KRT71", "KRT73",
                                         "TOP2A", "MCM5",
                                         "KRT14", "CXCL14",
                                         "KRT15", "COL17A1",
                                         "DIO2", "TCEAL2",
                                         "KRT16", "KRT6C",
                                         "SPINK5", "LY6D"),
                            group.by = "cluster_type", scale = TRUE,
                            scale.by = "radius", scale.min = NA, scale.max = NA) +
  ggplot2::scale_color_gradientn(colors = aquarius::color_gene) +
  ggplot2::theme(legend.position = "bottom",
                 legend.direction = "vertical",
                 # legend.justification = "bottom",
                 legend.box = "horizontal",
                 legend.box.margin = margin(0,25,0,0),
                 axis.title = element_blank(),
                 axis.ticks.y = element_blank(),
                 axis.text.y = element_blank(),
                 axis.line.y = element_blank(),
                 axis.text.x = element_text(angle = 45, hjust = 1, size = 11, color = "black"),
                 plot.margin = unit(c(0,0.5,0,0), "cm"))

p = ggplot2::ggplot(custom_order_cell_type, aes(y = cell_type, x = 0)) +
  ggplot2::geom_point(size = 0) +
  ggplot2::geom_segment(aes(y = 0.5, yend = 2.5, x = 0, xend = 0), size = 6, col = family_color["immune cells"]) +
  ggplot2::geom_segment(aes(y = 2.5, yend = 7.5, x = 0, xend = 0), size = 6, col = family_color["matrix"]) +
  ggplot2::geom_segment(aes(y = 7.5, yend = 11.5, x = 0, xend = 0), size = 6, col = family_color["non matrix"]) +
  ggplot2::scale_x_continuous(expand = c(0,0), limits = c(0,0)) +
  ggplot2::theme_classic() +
  ggplot2::theme(axis.text.x = element_blank(),
                 axis.ticks.x = element_blank(),
                 axis.title = element_blank(),
                 axis.line.x = element_blank(),
                 axis.text.y = element_text(size = 12, color = "black"),
                 plot.margin = unit(c(0.5,0,0,0.5), "cm"))

plot_list = patchwork::wrap_plots(p, plot_list,
                                  nrow = 1, widths = c(1, 25))
plot_list

IBL and ORS dataset

We load the IBL + ORS dataset :

sobj_iblors = readRDS(paste0(data_dir, "/5_wu/4_ibl_ors/iblmors_sobj.rds"))
sobj_iblors
## An object of class Seurat 
## 15541 features across 8026 samples within 1 assay 
## Active assay: RNA (15541 features, 2000 variable features)
##  6 dimensional reductions calculated: RNA_pca, RNA_pca_20_tsne, RNA_pca_20_umap, harmony, harmony_20_umap, harmony_20_tsne

This is the projection name to visualize cells :

name2D = "harmony_20_tsne"

To represent results from differential expression, we load the analyses results :

list_results = readRDS(paste0(data_dir, "/5_wu/4_ibl_ors/iblmors_list_results.rds"))

lapply(list_results, FUN = names)
## $IBL_vs_ORS
## [1] "mark"        "enrichr_ibl" "enrichr_ors" "gsea"

We defined cluster type :

cluster_type = table(sobj_iblors$cell_type, sobj_iblors$seurat_clusters) %>%
  prop.table(., margin = 2) %>%
  apply(., 2, which.max)
cluster_type = setNames(nm = names(cluster_type),
                        levels(sobj_iblors$cell_type)[cluster_type])

sobj_iblors$cluster_type = cluster_type[sobj_iblors$seurat_clusters]
sobj_iblors$cluster_type = factor(sobj_iblors$cluster_type,
                                  levels = c("IBL", "ORS"))

IBL + ORS figure

IBL + ORS on the full atlas :

sobj$cell_bc = colnames(sobj)
sobj_iblors$cell_bc = colnames(sobj_iblors)
sobj$is_iblors = dplyr::left_join(sobj@meta.data[, c("cell_bc", "percent.mt")],
                                  sobj_iblors@meta.data[, c("cell_bc", "cluster_type")],
                                  by = "cell_bc")[, "cluster_type"]

Seurat::DimPlot(sobj, reduction = name2D_atlas, pt.size = 0.000001,
                group.by = "is_iblors", order = FALSE) +
  ggplot2::scale_color_manual(values = c(color_markers[c("IBL", "ORS")], bg_color),
                              breaks = c("IBL", "ORS", NA), na.value = bg_color) +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_blank()) +
  Seurat::NoAxes() + Seurat::NoLegend()

Cluster type :

Seurat::DimPlot(sobj_iblors, reduction = name2D, pt.size = 1,
                group.by = "cluster_type", cols = color_markers) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() +
  Seurat::NoLegend()

DE genes between IBL and ORS :

mark = list_results$IBL_vs_ORS$mark
mark$gene_name = rownames(mark)
mark_label = rbind(
  # up-regulated in IBL
  mark %>% dplyr::top_n(., n = 20, wt = avg_logFC),
  # up-regulated in ORS
  mark %>% dplyr::top_n(., n = 20, wt = -avg_logFC),
  # representative and selective for IBL
  mark %>% dplyr::top_n(., n = 20, wt = (pct.1 - pct.2)),
  # representative and selective for ORS
  mark %>% dplyr::top_n(., n = 20, wt = -(pct.1 - pct.2))) %>%
  dplyr::distinct()
mark_label = mark_label[!grepl(rownames(mark_label), pattern = "^MT"), ]

avg_logFC_range = setNames(c(min(mark_label$avg_logFC), -1, 0, 1, max(mark_label$avg_logFC)),
                           nm = c("dodgerblue4", "dodgerblue3", "#B7B7B7", "firebrick3", "firebrick4"))


ggplot2::ggplot(mark, aes(x = pct.1, y = pct.2, col = avg_logFC)) +
  ggplot2::geom_abline(slope = 1, intercept = 0, lty = 2) +
  ggplot2::geom_point() +
  ggrepel::geom_label_repel(data = mark_label, max.overlaps = Inf,
                            aes(x = pct.1, y = pct.2, label = gene_name),
                            col = "black", fill = NA, size = 3.5, label.size = NA) +
  ggplot2::labs(x = "Enriched in IBL",
                y = "Enriched in ORS") +
  ggplot2::scale_color_gradientn(colors = names(avg_logFC_range),
                                 values = scales::rescale(unname(avg_logFC_range))) +
  ggplot2::theme_classic() +
  ggplot2::theme(aspect.ratio = 1)

GSEA plot :

the_gs_name = "REACTOME_KERATINIZATION" 
the_content = list_results$IBL_vs_ORS$gsea@result %>%
  dplyr::filter(ID == the_gs_name)
the_subtitle = paste0("\nNES : ", round(the_content$NES, 2),
                      " | pvalue : ", round(the_content$pvalue, 4),
                      " | set size : ", the_content$setSize, " genes")

enrichplot::gseaplot2(x = list_results$IBL_vs_ORS$gsea,
                      geneSetID = the_gs_name) +
  ggplot2::labs(title = the_gs_name,
                subtitle = the_subtitle) +
  ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold",
                                           margin = ggplot2::margin(3, 3, 5, 3)),
                 plot.subtitle = ggtext::element_markdown(hjust = 0.5,
                                                          size = 10))

the_gs_name = "HALLMARK_INTERFERON_GAMMA_RESPONSE" 
the_content = list_results$IBL_vs_ORS$gsea@result %>%
  dplyr::filter(ID == the_gs_name)
the_subtitle = paste0("\nNES : ", round(the_content$NES, 2),
                      " | pvalue : ", round(the_content$pvalue, 4),
                      " | set size : ", the_content$setSize, " genes")

enrichplot::gseaplot2(x = list_results$IBL_vs_ORS$gsea,
                      geneSetID = the_gs_name) +
  ggplot2::labs(title = the_gs_name,
                subtitle = the_subtitle) +
  ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold",
                                           margin = ggplot2::margin(3, 3, 5, 3)),
                 plot.subtitle = ggtext::element_markdown(hjust = 0.5,
                                                          size = 10))

Genes of interest :

genes = c("KRT16", "COL17A1", "DST", "KRT6B", "IL1R2", "WNT3",
          "IFI27", "CXCL14", "IGFBP3", "KRT15", "CD200")

plot_list = lapply(c(1:length(genes)), FUN = function(gene_id) {
  gene = genes[[gene_id]]
  
  sobj_iblors$my_gene = Seurat::FetchData(sobj_iblors, gene)[, 1] %>%
    aquarius::run_rescale(., new_min = 0, new_max = 10)
  
  Seurat::FeaturePlot(sobj_iblors, features = "my_gene", reduction = name2D, pt.size = 0.25) +
    ggplot2::scale_color_gradientn(colors = aquarius::color_gene,
                                   breaks = seq(0, 10, by = 2.5),
                                   labels = c("min", rep("", 3), "max")) +
    ggplot2::labs(title = gene) +
    ggplot2::theme(aspect.ratio = 1,
                   plot.title = element_text(hjust = 0.5, size = 17),
                   plot.subtitle = element_text(hjust = 0.5, size = 15),
                   legend.text = element_text(size = 15),
                   legend.position = "none") +
    Seurat::NoAxes()
})

plot_list
## [[1]]

## 
## [[2]]

## 
## [[3]]

## 
## [[4]]

## 
## [[5]]

## 
## [[6]]

## 
## [[7]]

## 
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R Session

show
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.6 LTS
## 
## Matrix products: default
## BLAS:   /usr/local/lib/R/lib/libRblas.so
## LAPACK: /usr/local/lib/R/lib/libRlapack.so
## 
## locale:
## [1] C
## 
## attached base packages:
##  [1] parallel  stats4    grid      stats     graphics  grDevices utils    
##  [8] datasets  methods   base     
## 
## other attached packages:
##  [1] org.Mm.eg.db_3.10.0   AnnotationDbi_1.48.0  IRanges_2.20.2       
##  [4] S4Vectors_0.24.4      Biobase_2.46.0        BiocGenerics_0.32.0  
##  [7] ComplexHeatmap_2.14.0 ggplot2_3.3.5         patchwork_1.1.2      
## [10] dplyr_1.0.7          
## 
## loaded via a namespace (and not attached):
##   [1] softImpute_1.4              graphlayouts_0.7.0         
##   [3] pbapply_1.4-2               lattice_0.20-41            
##   [5] haven_2.3.1                 dyndimred_1.0.3            
##   [7] vctrs_0.3.8                 usethis_2.0.1              
##   [9] dynwrap_1.2.1               blob_1.2.1                 
##  [11] survival_3.2-13             prodlim_2019.11.13         
##  [13] dynutils_1.0.5              later_1.3.0                
##  [15] DBI_1.1.1                   R.utils_2.11.0             
##  [17] SingleCellExperiment_1.8.0  rappdirs_0.3.3             
##  [19] uwot_0.1.8                  dqrng_0.2.1                
##  [21] jpeg_0.1-8.1                zlibbioc_1.32.0            
##  [23] pspline_1.0-18              pcaMethods_1.78.0          
##  [25] mvtnorm_1.1-1               htmlwidgets_1.5.4          
##  [27] GlobalOptions_0.1.2         future_1.22.1              
##  [29] UpSetR_1.4.0                laeken_0.5.2               
##  [31] leiden_0.3.3                clustree_0.4.3             
##  [33] lmds_0.1.0                  scater_1.14.6              
##  [35] irlba_2.3.3                 markdown_1.1               
##  [37] DEoptimR_1.0-9              tidygraph_1.1.2            
##  [39] Rcpp_1.0.9                  readr_2.0.2                
##  [41] KernSmooth_2.23-17          carrier_0.1.0              
##  [43] promises_1.1.0              gdata_2.18.0               
##  [45] DelayedArray_0.12.3         limma_3.42.2               
##  [47] graph_1.64.0                RcppParallel_5.1.4         
##  [49] Hmisc_4.4-0                 fs_1.5.2                   
##  [51] RSpectra_0.16-0             fastmatch_1.1-0            
##  [53] ranger_0.12.1               digest_0.6.25              
##  [55] png_0.1-7                   sctransform_0.2.1          
##  [57] cowplot_1.0.0               DOSE_3.12.0                
##  [59] here_1.0.1                  TInGa_0.0.0.9000           
##  [61] dynplot_1.1.0               ggraph_2.0.3               
##  [63] pkgconfig_2.0.3             GO.db_3.10.0               
##  [65] DelayedMatrixStats_1.8.0    gower_0.2.1                
##  [67] ggbeeswarm_0.6.0            iterators_1.0.12           
##  [69] DropletUtils_1.6.1          reticulate_1.26            
##  [71] clusterProfiler_3.14.3      SummarizedExperiment_1.16.1
##  [73] circlize_0.4.15             beeswarm_0.4.0             
##  [75] GetoptLong_1.0.5            xfun_0.35                  
##  [77] bslib_0.3.1                 zoo_1.8-10                 
##  [79] tidyselect_1.1.0            GA_3.2                     
##  [81] reshape2_1.4.4              purrr_0.3.4                
##  [83] ica_1.0-2                   pcaPP_1.9-73               
##  [85] viridisLite_0.3.0           rtracklayer_1.46.0         
##  [87] rlang_1.0.2                 hexbin_1.28.1              
##  [89] jquerylib_0.1.4             dyneval_0.9.9              
##  [91] glue_1.4.2                  waldo_0.3.1                
##  [93] RColorBrewer_1.1-2          matrixStats_0.56.0         
##  [95] stringr_1.4.0               lava_1.6.7                 
##  [97] europepmc_0.3               DESeq2_1.26.0              
##  [99] recipes_0.1.17              labeling_0.3               
## [101] httpuv_1.5.2                class_7.3-17               
## [103] BiocNeighbors_1.4.2         DO.db_2.9                  
## [105] annotate_1.64.0             jsonlite_1.7.2             
## [107] XVector_0.26.0              bit_4.0.4                  
## [109] mime_0.9                    aquarius_0.1.5             
## [111] Rsamtools_2.2.3             gridExtra_2.3              
## [113] gplots_3.0.3                stringi_1.4.6              
## [115] processx_3.5.2              gsl_2.1-6                  
## [117] bitops_1.0-6                cli_3.0.1                  
## [119] batchelor_1.2.4             RSQLite_2.2.0              
## [121] randomForest_4.6-14         tidyr_1.1.4                
## [123] data.table_1.14.2           rstudioapi_0.13            
## [125] units_0.7-2                 GenomicAlignments_1.22.1   
## [127] nlme_3.1-147                qvalue_2.18.0              
## [129] scran_1.14.6                locfit_1.5-9.4             
## [131] scDblFinder_1.1.8           listenv_0.8.0              
## [133] ggthemes_4.2.4              gridGraphics_0.5-0         
## [135] R.oo_1.24.0                 dbplyr_1.4.4               
## [137] TTR_0.24.2                  readxl_1.3.1               
## [139] lifecycle_1.0.1             timeDate_3043.102          
## [141] ggpattern_0.3.1             munsell_0.5.0              
## [143] cellranger_1.1.0            R.methodsS3_1.8.1          
## [145] proxyC_0.1.5                visNetwork_2.0.9           
## [147] caTools_1.18.0              codetools_0.2-16           
## [149] GenomeInfoDb_1.22.1         vipor_0.4.5                
## [151] lmtest_0.9-38               msigdbr_7.5.1              
## [153] htmlTable_1.13.3            triebeard_0.3.0            
## [155] lsei_1.2-0                  xtable_1.8-4               
## [157] ROCR_1.0-7                  classInt_0.4-3             
## [159] BiocManager_1.30.10         scatterplot3d_0.3-41       
## [161] abind_1.4-5                 farver_2.0.3               
## [163] parallelly_1.28.1           RANN_2.6.1                 
## [165] askpass_1.1                 GenomicRanges_1.38.0       
## [167] RcppAnnoy_0.0.16            tibble_3.1.5               
## [169] ggdendro_0.1-20             cluster_2.1.0              
## [171] future.apply_1.5.0          Seurat_3.1.5               
## [173] dendextend_1.15.1           Matrix_1.3-2               
## [175] ellipsis_0.3.2              prettyunits_1.1.1          
## [177] lubridate_1.7.9             ggridges_0.5.2             
## [179] igraph_1.2.5                RcppEigen_0.3.3.7.0        
## [181] fgsea_1.12.0                remotes_2.4.2              
## [183] scBFA_1.0.0                 destiny_3.0.1              
## [185] VIM_6.1.1                   testthat_3.1.0             
## [187] htmltools_0.5.2             BiocFileCache_1.10.2       
## [189] yaml_2.2.1                  utf8_1.1.4                 
## [191] plotly_4.9.2.1              XML_3.99-0.3               
## [193] ModelMetrics_1.2.2.2        e1071_1.7-3                
## [195] foreign_0.8-76              withr_2.5.0                
## [197] fitdistrplus_1.0-14         BiocParallel_1.20.1        
## [199] xgboost_1.4.1.1             bit64_4.0.5                
## [201] foreach_1.5.0               robustbase_0.93-9          
## [203] Biostrings_2.54.0           GOSemSim_2.13.1            
## [205] rsvd_1.0.3                  memoise_2.0.0              
## [207] evaluate_0.18               forcats_0.5.0              
## [209] rio_0.5.16                  geneplotter_1.64.0         
## [211] tzdb_0.1.2                  caret_6.0-86               
## [213] ps_1.6.0                    DiagrammeR_1.0.6.1         
## [215] curl_4.3                    fdrtool_1.2.15             
## [217] fansi_0.4.1                 highr_0.8                  
## [219] urltools_1.7.3              xts_0.12.1                 
## [221] GSEABase_1.48.0             acepack_1.4.1              
## [223] edgeR_3.28.1                checkmate_2.0.0            
## [225] scds_1.2.0                  cachem_1.0.6               
## [227] npsurv_0.4-0                babelgene_22.3             
## [229] rjson_0.2.20                openxlsx_4.1.5             
## [231] ggrepel_0.9.1               clue_0.3-60                
## [233] rprojroot_2.0.2             stabledist_0.7-1           
## [235] tools_3.6.3                 sass_0.4.0                 
## [237] nichenetr_1.1.1             magrittr_2.0.1             
## [239] RCurl_1.98-1.2              proxy_0.4-24               
## [241] car_3.0-11                  ape_5.3                    
## [243] ggplotify_0.0.5             xml2_1.3.2                 
## [245] httr_1.4.2                  assertthat_0.2.1           
## [247] rmarkdown_2.18              boot_1.3-25                
## [249] globals_0.14.0              R6_2.4.1                   
## [251] Rhdf5lib_1.8.0              nnet_7.3-14                
## [253] RcppHNSW_0.2.0              progress_1.2.2             
## [255] genefilter_1.68.0           statmod_1.4.34             
## [257] gtools_3.8.2                shape_1.4.6                
## [259] sf_1.0-3                    HDF5Array_1.14.4           
## [261] BiocSingular_1.2.2          rhdf5_2.30.1               
## [263] splines_3.6.3               AUCell_1.8.0               
## [265] carData_3.0-4               colorspace_1.4-1           
## [267] generics_0.1.0              base64enc_0.1-3            
## [269] dynfeature_1.0.0            smoother_1.1               
## [271] gridtext_0.1.1              pillar_1.6.3               
## [273] tweenr_1.0.1                sp_1.4-1                   
## [275] ggplot.multistats_1.0.0     rvcheck_0.1.8              
## [277] GenomeInfoDbData_1.2.2      plyr_1.8.6                 
## [279] gtable_0.3.0                zip_2.2.0                  
## [281] knitr_1.41                  latticeExtra_0.6-29        
## [283] biomaRt_2.42.1              fastmap_1.1.0              
## [285] ADGofTest_0.3               copula_1.0-0               
## [287] doParallel_1.0.15           vcd_1.4-8                  
## [289] babelwhale_1.0.1            openssl_1.4.1              
## [291] scales_1.1.1                backports_1.2.1            
## [293] ipred_0.9-12                enrichplot_1.6.1           
## [295] hms_1.1.1                   ggforce_0.3.1              
## [297] Rtsne_0.15                  shiny_1.7.1                
## [299] gridpattern_0.3.1           numDeriv_2016.8-1.1        
## [301] polyclip_1.10-0             lazyeval_0.2.2             
## [303] Formula_1.2-3               tsne_0.1-3                 
## [305] crayon_1.3.4                MASS_7.3-54                
## [307] pROC_1.16.2                 viridis_0.5.1              
## [309] dynparam_1.0.0              rpart_4.1-15               
## [311] zinbwave_1.8.0              compiler_3.6.3             
## [313] ggtext_0.1.0
---
title: "HS project"
subtitle: "Figures"
author: "Audrey"
date: "`r format(Sys.time(), '%Y-%m-%d')`"
output:
  html_document:
    code_folding: show
    code_download: true
    toc: true
    toc_float: true
    number_sections: false
---

<style>
body {
text-align: justify}
</style>

<!-- Automatically computes and prints in the output the running time for any code chunk -->
```{r, echo=FALSE}
# https://github.com/rstudio/rmarkdown/issues/1453
hooks = knitr::knit_hooks$get()
hook_foldable = function(type) {
  force(type)
  function(x, options) {
    res = hooks[[type]](x, options)
    
    if (isFALSE(options[[paste0("fold_", type)]])) return(res)
    
    paste0(
      "<details><summary>", "show", "</summary>\n\n",
      res,
      "\n\n</details>"
    )
  }
}
knitr::knit_hooks$set(
  output = hook_foldable("output"),
  plot = hook_foldable("plot"),
  time_it = local({
    now = NULL
    function(before, options) {
      if (options$time_it) {
        if (before) {
          now <= Sys.time()
        } else {
          res = difftime(Sys.time(), now, units = "secs")
          paste("(Time to run :", round(res, digits = 2), "s)")
        }
      }
    }
  })
)
```

<!-- Set default parameters for all chunks -->
```{r, setup, include = FALSE}
set.seed(1337L)
knitr::opts_chunk$set(echo = TRUE, # display code
                      # display chunk output
                      message = FALSE,
                      warning = FALSE,
                      fold_output = FALSE, # useful for sessionInfo()
                      fold_plot = FALSE,
                      
                      # figure settings
                      fig.align = 'center',
                      fig.width = 20,
                      fig.height = 15,
                      
                      # something about seed, chunk and Rmarkdown compilation
                      # https://stackoverflow.com/questions/39417003/long-vectors-not-supported-yet-error-in-rmd-but-not-in-r-script
                      # cache = TRUE,
                      cache.lazy = FALSE, 
                      
                      # add runtime after chunk
                      time_it = FALSE,
                      
                      # save figures in PDF in a separate folder
                      dev = c('png', 'pdf'), # tiff or pdf alone renders bad in html
                      # dpi = 300,
                      fig.path = "figures_detail/",
                      pdf.options(encoding = "ISOLatin9.enc"))
```


This file is used to prepare the figures for the paper.

```{r library}
library(dplyr)
library(patchwork)
library(ggplot2)
library(ComplexHeatmap)
library(org.Mm.eg.db)

.libPaths()
```


# Preparation

Here are the folders where analyzes are stored :

```{r locations}
data_dir = "./.."
list.files(data_dir)
```


We load the dataset containing all cells :

```{r load_all_sobj}
sobj = readRDS(paste0(data_dir, "/3_combined/hs_hd_sobj.rds"))
sobj
```

These are all the samples analyzed :

```{r sample_info, class.source = 'fold-hide', fig.width = 8, fig.height = 3}
sample_info = readRDS(paste0(data_dir, "/1_metadata/hs_hd_sample_info.rds"))

# Nb cells by dataset
to_plot = table(sobj$sample_identifier) %>%
  as.data.frame.table(., stringsAsFactors = FALSE) %>%
  `colnames<-`(c("sample_identifier", "nb_cells")) %>%
  dplyr::left_join(x = ., y = sample_info, by = "sample_identifier") 

# patchwork
plot_list = aquarius::fig_plot_gb(to_plot, title = "Available datasets")
patchwork::wrap_plots(plot_list) +
  patchwork::plot_layout(design = "A\nB", heights = c(0.1,5)) &
  ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold", size = 15))
```

These are the custom colors for cell populations :

```{r color_markers, fig.width = 10, fig.height = 1, class.source = "fold-hide"}
color_markers = readRDS(paste0(data_dir, "/1_metadata/hs_hd_color_markers.rds"))
color_markers = color_markers[names(color_markers) != "melanocytes"]
ors_color = color_markers["ORS"]
color_markers["ORS"] = color_markers["IFE"] 
color_markers["IFE"] = ors_color
color_markers["B cells"] = "chocolate3"
rm(ors_color)

# re-order
color_markers = color_markers[c("CD4 T cells", "CD8 T cells", "Langerhans cells", "macrophages", "B cells",
                                "cuticle", "cortex", "medulla", "IRS", "proliferative",
                                "HFSC", "ORS", "IBL", "IFE", "sebocytes")]

data.frame(cell_type = names(color_markers),
           color = unlist(color_markers)) %>%
  ggplot2::ggplot(., aes(x = cell_type, y = 0, fill = cell_type)) +
  ggplot2::geom_point(pch = 21, size = 5) +
  ggplot2::scale_fill_manual(values = unlist(color_markers), breaks = names(color_markers)) +
  ggplot2::theme_classic() +
  ggplot2::theme(legend.position = "none",
                 axis.line = element_blank(),
                 axis.title = element_blank(),
                 axis.ticks = element_blank(),
                 axis.text.y = element_blank(),
                 axis.text.x = element_text(hjust = 1, angle = 20))
```

We define custom colors for sample type :

```{r sample_type_colors, fig.width = 3, fig.height = 0.75, class.source = "fold-hide"}
sample_type_colors = setNames(nm = levels(sample_info$sample_type),
                              c("#C55F40", "#2C78E6"))

data.frame(cell_type = names(sample_type_colors),
           color = unlist(sample_type_colors)) %>%
  ggplot2::ggplot(., aes(x = cell_type, y = 0, fill = cell_type)) +
  ggplot2::geom_point(pch = 21, size = 5) +
  ggplot2::scale_fill_manual(values = unlist(sample_type_colors), breaks = names(sample_type_colors)) +
  ggplot2::theme_classic() +
  ggplot2::theme(legend.position = "none",
                 axis.line = element_blank(),
                 axis.title = element_blank(),
                 axis.ticks = element_blank(),
                 axis.text.y = element_blank())
```

We set a background color :

```{r bg_color}
bg_color = "gray94"
```


This is the correspondence between cell types and cell families, and custom colors to color cells by cell family :

```{r cell_family}
custom_order_cell_type = data.frame(
  cell_type = names(color_markers),
  cell_family = c(rep("immune cells", 5),
                  rep("matrix", 5),
                  rep("non matrix", 5)),
  stringsAsFactors = FALSE)
custom_order_cell_type$cell_type = factor(custom_order_cell_type$cell_type,
                                          levels = custom_order_cell_type$cell_type)
rownames(custom_order_cell_type) = custom_order_cell_type$cell_type

family_color = c("immune cells" = "slateblue1",
                 "matrix" = "mediumseagreen",
                 "non matrix" = "firebrick3")
```

We load markers to display on a dotplot to assess cell type annotation :

```{r dotplot_markers}
dotplot_markers = readRDS(paste0(data_dir, "/1_metadata/hs_hd_dotplot_markers.rds"))
dotplot_markers = dotplot_markers[names(dotplot_markers) != "melanocytes"]
lengths(dotplot_markers)
```

Custom functions to display gene expression on the heatmap :

```{r color_fun, class.source = "fold-hide"}
color_fun = function(one_gene) {
  gene_range = range(ht_annot[, one_gene])
  gene_palette = circlize::colorRamp2(colors = c("#FFFFFF", aquarius::color_gene[-1]),
                                      breaks = seq(from = gene_range[1], to = gene_range[2],
                                                   length.out = length(aquarius::color_gene)))
  return(gene_palette)
}
```


# All samples

## Settings

This is the projection name to visualize cells :

```{r all_sobj_name2D}
name2D = "harmony_38_tsne"
name2D_atlas = name2D
```

## Preparation

We make a low resolutive clustering for the heatmap :

```{r cluster_all}
sobj = Seurat::FindClusters(sobj, resolution = 0.4)

length(levels(sobj$seurat_clusters))
```


We define cluster type and cluster family :

```{r cluster_type_family_all, fig.width = 7, fig.height = 5}
sobj$cell_type = sobj$cell_type %>%
  as.character() %>%
  factor(., levels = names(color_markers))

cluster_type = table(sobj$cell_type, sobj$seurat_clusters) %>%
  prop.table(., margin = 2) %>%
  apply(., 2, which.max)
cluster_type = setNames(nm = names(cluster_type),
                        levels(sobj$cell_type)[cluster_type])

sobj$cluster_type = cluster_type[sobj$seurat_clusters]
sobj$cluster_type = factor(sobj$cluster_type,
                           levels = levels(sobj$cell_type))
sobj$cluster_family = custom_order_cell_type[sobj$cluster_type, "cell_family"]
sobj$cluster_family = factor(sobj$cluster_family,
                             levels = names(family_color))
```


## Figures

Project name :

```{r fig1_sample_identifier, fig.width = 4, fig.height = 4}
# Random order
set.seed(1234)
rnd_order = sample(colnames(sobj), replace = FALSE, size = ncol(sobj))

# Extract coordinates
cells_coord = sobj@reductions[[name2D]]@cell.embeddings %>%
  as.data.frame() %>%
  `colnames<-`(c("Dim1", "Dim2"))
cells_coord$project_name = sobj$project_name
cells_coord = cells_coord[(rnd_order), ]

# Plot
ggplot2::ggplot(cells_coord, aes(x = Dim1, y = Dim2, col = project_name)) +
  ggplot2::geom_point(size = 0.5) +
  ggplot2::scale_color_manual(values = sample_info$color,
                              breaks = sample_info$project_name) +
  ggplot2::theme_void() +
  ggplot2::theme(aspect.ratio = 1,
                 legend.position = "none")
```


Sample type :

```{r fig1_sample_type, fig.width = 4, fig.height = 4}
# Extract coordinates
cells_coord = sobj@reductions[[name2D]]@cell.embeddings %>%
  as.data.frame() %>%
  `colnames<-`(c("Dim1", "Dim2"))
cells_coord$sample_type = sobj$sample_type
cells_coord = cells_coord[(rnd_order), ]

# Plot
ggplot2::ggplot(cells_coord, aes(x = Dim1, y = Dim2, col = sample_type)) +
  ggplot2::geom_point(size = 0.5) +
  ggplot2::scale_color_manual(values = sample_type_colors,
                              breaks = names(sample_type_colors)) +
  ggplot2::theme_void() +
  ggplot2::theme(aspect.ratio = 1,
                 legend.position = "none")
```

Cluster :

```{r fig1_cluster, fig.width = 4, fig.height = 4}
grey_palette = setNames(nm = levels(sobj$seurat_clusters),
                        rep("#D9D9D9", length(levels(sobj$seurat_clusters))))
grey_palette[c("7", "16", "1", "12", "11", "10", "15")] = "#BDBDBD"
grey_palette[c("16", "14", "5", "9")] = "#969696"

Seurat::DimPlot(sobj, reduction = name2D, pt.size = 0.4,
                group.by = "seurat_clusters", cols = grey_palette,
                label = TRUE, label.size = 6) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() +
  Seurat::NoLegend()
```

Cell type annotation :

```{r fig1_cell_type, fig.width = 4, fig.height = 4}
Seurat::DimPlot(sobj, reduction = name2D, pt.size = 0.5,
                group.by = "cell_type", cols = color_markers) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() +
  Seurat::NoLegend()
```

Cluster family annotation :

```{r fig1_cluster_family, fig.width = 6, fig.height = 6}
Seurat::DimPlot(sobj, reduction = name2D, pt.size = 0.5,
                group.by = "cluster_family", cols = family_color) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() +
  Seurat::NoLegend()
```

Cell type annotation split by condition :

```{r fig1_cell_type_split, fig.width = 8, fig.height = 5}
plot_list = aquarius::plot_split_dimred(sobj, reduction = name2D,
                                        group_by = "cell_type",
                                        group_color = color_markers,
                                        split_by = "sample_type",
                                        split_color = sample_type_colors,
                                        bg_color = bg_color)

patchwork::wrap_plots(plot_list) &
  Seurat::NoLegend()
```

Gene expression to assess annotation :

```{r fig1_gene_family, fig.width = 4, fig.height = 4}
genes = c("PTPRC", "MSX2", "KRT14")
names(genes) = c("immune cells", "matrix cells", "non-matrix cells")

plot_list = lapply(c(1:length(genes)), FUN = function(gene_id) {
  gene = genes[[gene_id]]
  pop = names(genes)[gene_id]
  
  sobj$my_gene = Seurat::FetchData(sobj, gene)[, 1] %>%
    aquarius::run_rescale(., new_min = 0, new_max = 10)
  
  Seurat::FeaturePlot(sobj, features = "my_gene", reduction = name2D) +
    ggplot2::scale_color_gradientn(colors = aquarius::color_gene,
                                   breaks = seq(0, 10, by = 2.5),
                                   labels = c("min", rep("", 3), "max")) +
    ggplot2::labs(title = gene) + 
    # subtitle = pop) +
    ggplot2::theme(aspect.ratio = 1,
                   plot.title = element_text(hjust = 0.5, size = 17),
                   plot.subtitle = element_text(hjust = 0.5, size = 15),
                   legend.text = element_text(size = 15),
                   legend.position = "none") +
    Seurat::NoAxes()
})

plot_list
```

Barplot by cluster family :

```{r fig1_barplot_family, fig.width = 3.5, fig.height = 3.5}
quantif = table(sobj$sample_identifier) %>%
  as.data.frame.table() %>%
  `colnames<-`(c("Sample", "nb_cells"))

aquarius::plot_barplot(df = table(sobj$sample_identifier,
                                  sobj$cluster_family) %>%
                         as.data.frame.table() %>%
                         `colnames<-`(c("Sample", "Cell Type", "Number")),
                       x = "Sample", y = "Number", fill = "Cell Type",
                       position = position_fill()) +
  ggplot2::geom_label(data = quantif, inherit.aes = FALSE,
                      aes(x = .data$Sample, y = 1.05, label = .data$nb_cells),
                      label.size = 0, size = 4) +
  ggplot2::scale_fill_manual(values = unlist(family_color),
                             breaks = names(family_color),
                             name = "Cell Family") +
  ggplot2::scale_y_continuous(breaks = seq(0, 1, by = 0.25),
                              labels = paste0(seq(0, 100, by = 25), sep = " %"),
                              expand = ggplot2::expansion(add = c(0, 0.05))) +
  ggplot2::theme(axis.title.y = element_blank(),
                 axis.line.x = element_line(colour = "lightgray"),
                 text = element_text(size = 15),
                 axis.text.x = element_text(margin = margin(t = 25, r = 0, b = 0, l = 0)),
                 legend.position = "none")
```

Heatmap of cluster proportion by sample :

```{r fig1_heatmap_prop, fig.width = 9, fig.height = 5, class.source = "fold-hide"}
group_by = "seurat_clusters"

cluster_by_sample = table(sobj$sample_identifier,
                          sobj@meta.data[, group_by]) %>%
  prop.table(margin = 1) %>%
  as.matrix()

## Right annotation : number of cells by dataset
ht_annot = table(sobj$sample_identifier) %>%
  as.data.frame.table() %>%
  `colnames<-`(c("sample_identifier", "nb_cells")) %>%
  `rownames<-`(.$sample_identifier) %>%
  dplyr::select(-sample_identifier)

ha_right = ComplexHeatmap::HeatmapAnnotation(
  df = ht_annot,
  which = "row",
  show_legend = TRUE,
  annotation_name_side = "top",
  col = list(nb_cells  = circlize::colorRamp2(colors = RColorBrewer::brewer.pal(name = "Greys", n = 9),
                                              breaks = seq(from = range(ht_annot$nb_cells)[1],
                                                           to = range(ht_annot$nb_cells)[2],
                                                           length.out = 9))))

## Left annotation : gender
ha_left = ComplexHeatmap::HeatmapAnnotation(
  gender = sample_info$gender,
  which = "row",
  show_legend = TRUE,
  annotation_name_side = "top",
  col = list(gender = setNames(nm = c("F", "M"),
                               c("lightcyan3", "navyblue"))))

## Top annotation : main cell type in this cluster
ht_annot = table(sobj$sample_identifier,
                 sobj@meta.data[, group_by]) %>%
  prop.table(margin = 1) %>%
  as.matrix()

ht_annot = table(sobj$cell_type,
                 sobj@meta.data[, group_by]) %>%
  prop.table(., margin = 2) %>%
  apply(., 2, which.max)
ht_annot = data.frame(row.names = names(ht_annot),
                      cell_type = names(color_markers)[ht_annot],
                      stringsAsFactors = FALSE)
ht_annot = dplyr::left_join(ht_annot, custom_order_cell_type, by = "cell_type") %>%
  # Simplification for matrix
  dplyr::mutate(cell_type = ifelse(cell_type %in% c("medulla", "cortex", "cuticle"), yes = "hair shaft", no = cell_type)) %>%
  # Simplification for T cells
  dplyr::mutate(cell_type = ifelse(cell_type %in% c("CD4 T cells", "CD8 T cells"), yes = "T cells", no = cell_type)) %>%
  # Simplification for APC
  dplyr::mutate(cell_type = ifelse(cell_type %in% c("Langerhans cells", "macrophages"), yes = "APC", no = cell_type)) %>%
  # Add color
  dplyr::mutate(color = as.character(color_markers[cell_type])) %>%
  dplyr::mutate(color = ifelse(cell_type == "hair shaft", yes = "#FFB6C1", no = color)) %>%
  dplyr::mutate(color = ifelse(cell_type == "T cells", yes = "#8A6EE6", no = color)) %>%
  dplyr::mutate(color = ifelse(cell_type == "APC", yes = "#9CAA4B", no = color))

ha_top = ComplexHeatmap::HeatmapAnnotation(
  # cell_type = ht_annot$cell_type,
  cell_family = ht_annot$cell_family,
  which = "column",
  show_legend = TRUE,
  show_annotation_name = FALSE,
  # annotation_name_side = "left",
  col = list(#cell_type = setNames(nm = ht_annot$cell_type,
    #                      ht_annot$color),
    cell_family = family_color
  ))

## Assemble heatmap
ht = ComplexHeatmap::Heatmap(cluster_by_sample,
                             heatmap_legend_param = list(title = "Proportion",
                                                         col = c("#2166AC", "#F7F7F7", "#B2182B")),
                             # bottom_annotation = ha_bottom,
                             right_annotation = ha_right,
                             left_annotation = ha_left,
                             top_annotation = ha_top,
                             cluster_rows = TRUE,
                             cluster_columns = TRUE,
                             row_title = "Sample",
                             row_names_gp = grid::gpar(names = sample_info$sample_identifier,
                                                       col = sample_info$color,
                                                       fontface = "bold"),
                             column_title = "Cluster",
                             column_names_centered = TRUE,
                             row_names_side = "left",
                             column_names_side = "top",
                             column_names_rot = 0)

## Draw !
ComplexHeatmap::draw(ht, merge_legends = TRUE)
```

For the dotplot, we clarify clusters and cell type annotation :

```{r improve_custom_order_cell_type}
cell_type_in_cluster = table(sobj$cell_type, sobj$seurat_clusters) %>%
  prop.table(., margin = 1) %>%
  apply(., 1, which.max)
cell_type_in_cluster = cell_type_in_cluster - 1

missing_cluster = setdiff(levels(sobj$seurat_clusters),
                          cell_type_in_cluster)

cell_type_in_cluster = data.frame(cell_type = c(names(cell_type_in_cluster), cluster_type[missing_cluster]),
                                  cluster_id = c(cell_type_in_cluster, names(cluster_type[missing_cluster])),
                                  stringsAsFactors = FALSE, row.names = NULL) %>%
  dplyr::mutate(cluster_id = as.numeric(cluster_id)) %>%
  dplyr::arrange(cell_type, cluster_id)

custom_order_cell_type$clusters = custom_order_cell_type %>%
  apply(., MARGIN = 1, FUN = function(one_row) {
    cell_type = one_row["cell_type"]
    clusters = cell_type_in_cluster %>%
      dplyr::filter(.data$cell_type == .env$cell_type) %>%
      dplyr::pull(cluster_id)
    
    cell_type_cluster = paste0(cell_type, " (", paste0(clusters, collapse = ", "), ")")
    
    return(cell_type_cluster)
  }) %>%
  factor(., levels = .)

custom_order_cell_type
```

Dotplot :

```{r fig1_dotplot, fig.width = 8, fig.height = 8.5, class.source = "fold-hide"}
plot_list = aquarius::plot_dotplot(sobj,
                                   markers = c("PTPRC",
                                               "CD3E", "CD4",
                                               "CD3E", "CD8A",
                                               "CD207", "AIF1",
                                               "TREM2", "MSR1",
                                               "CD79A", "CD79B",
                                               # "PRDM1", "KRT85",
                                               "MSX2",
                                               "KRT32", "KRT35",
                                               "KRT31", "PRR9",
                                               "BAMBI", "ALDH1A3",
                                               "KRT71", "KRT73",
                                               "TOP2A", "MCM5",
                                               "KRT14", "CXCL14",
                                               "KRT15", "COL17A1",
                                               "DIO2", "TCEAL2",
                                               "KRT16", "KRT6C",
                                               "SPINK5", "LY6D",
                                               "CLMP", "PPARG"),
                                   assay = "RNA", column_name = "cell_type", nb_hline = 0) +
  ggplot2::scale_color_gradientn(colors = aquarius::color_gene) +
  ggplot2::theme(legend.position = "left",
                 legend.justification = "bottom",
                 legend.box = "vertical",
                 legend.box.margin = margin(0,70,0,0),
                 axis.title = element_blank(),
                 axis.ticks.x = element_blank(),
                 axis.text.x = element_blank(),
                 axis.line.x = element_blank(),
                 plot.margin = unit(rep(0, 4), "cm"))

p = ggplot2::ggplot(custom_order_cell_type, aes(x = clusters, y = 0)) +
  ggplot2::geom_point(size = 0) +
  ggplot2::geom_segment(aes(x = 0.5, xend = 5.5, y = 0, yend = 0), size = 6, col = family_color["immune cells"]) +
  ggplot2::geom_segment(aes(x = 5.5, xend = 10.5, y = 0, yend = 0), size = 6, col = family_color["matrix"]) +
  ggplot2::geom_segment(aes(x = 10.5, xend = 15.5, y = 0, yend = 0), size = 6, col = family_color["non matrix"]) +
  ggplot2::scale_y_continuous(expand = c(0,0), limits = c(0,0)) +
  ggplot2::theme_classic() +
  ggplot2::theme(axis.text.y = element_blank(),
                 axis.ticks.y = element_blank(),
                 axis.title = element_blank(),
                 axis.line.y = element_blank(),
                 axis.text.x = element_text(angle = 45, hjust = 1, size = 10, color = "black"),
                 plot.margin = unit(c(0,0.5,0.5,0), "cm"))

plot_list = patchwork::wrap_plots(plot_list, p,
                                  ncol = 1, heights = c(25, 1))
plot_list
```


# Immune cells

## Settings

We load the immune cells dataset :

```{r sobj_ic}
sobj_ic = readRDS(paste0(data_dir, "/4_zoom/1_zoom_immune/immune_cells_sobj.rds"))
sobj_ic
```


This is the projection name to visualize cells :

```{r sobj_name2D_ic}
name2D = "harmony_20_tsne"
```

To represent results from differential expression, we load the analyses results :

```{r list_results_ic}
list_results = readRDS(paste0(data_dir, "/4_zoom/1_zoom_immune/immune_cells_list_results.rds"))

lapply(list_results, FUN = names)
```


## Preparation

We defined cluster type and cluster family :

```{r cluster_type_family_ic, fig.width = 7, fig.height = 5}
cluster_type = table(sobj_ic$cell_type, sobj_ic$seurat_clusters) %>%
  prop.table(., margin = 2) %>%
  apply(., 2, which.max)
cluster_type = setNames(nm = names(cluster_type),
                        levels(sobj_ic$cell_type)[cluster_type])

sobj_ic$cluster_type = cluster_type[sobj_ic$seurat_clusters]
sobj_ic$cluster_type = factor(sobj_ic$cluster_type,
                              levels = levels(sobj_ic$cell_type))
sobj_ic$cluster_family = custom_order_cell_type[sobj_ic$cluster_type, "cell_family"]
sobj_ic$cluster_family = factor(sobj_ic$cluster_family,
                                levels = names(family_color))
```


## Figures

Control cells on the full atlas :

```{r fig_ic_location, fig.width = 2, fig.height = 2}
sobj$is_immune = (colnames(sobj) %in% colnames(sobj_ic))

Seurat::DimPlot(sobj, reduction = name2D_atlas, pt.size = 0.000001,
                group.by = "is_immune", order = "TRUE") +
  ggplot2::scale_color_manual(values = c(family_color[["immune cells"]], bg_color),
                              breaks = c(TRUE, FALSE)) +
  ggplot2::labs(title = "Immune cells",
                subtitle = paste0(ncol(sobj_ic), " cells")) +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5, face = "bold"),
                 plot.subtitle = element_text(hjust = 0.5)) +
  Seurat::NoAxes() + Seurat::NoLegend()
```


Violin plot of IL1B in macrophages :

```{r fig_ic_mac_il1b, fig.width = 2, fig.height = 2.5}
subsobj = subset(sobj_ic, seurat_clusters == 2)
table(subsobj$sample_type)

il1b_hs = subsobj@assays$RNA@data["IL1B", subsobj$sample_type == "HS"]
il1b_hd = subsobj@assays$RNA@data["IL1B", subsobj$sample_type == "HD"]
il1b_hs_VS_il1b_hd = stats::t.test(il1b_hs, il1b_hd)
il1b_hs_VS_il1b_hd

Seurat::VlnPlot(subsobj, group.by = "sample_type", pt.size = 0.3,
                features = "IL1B", cols = sample_type_colors) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")
```

Split by sample :

```{r fig_ic_mac_il1b_split, fig.width = 6, fig.height = 4}
Seurat::VlnPlot(subsobj, group.by = "sample_identifier",
                features = "IL1B", cols = sample_info$color) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")
```

Violin plot of IL1B in macrophages :

```{r fig_ic_mac_il6, fig.width = 2, fig.height = 2.5}
il6_hs = subsobj@assays$RNA@data["IL6", subsobj$sample_type == "HS"]
il6_hd = subsobj@assays$RNA@data["IL6", subsobj$sample_type == "HD"]
il6_hs_VS_il6_hd = stats::t.test(il6_hs, il6_hd)
il6_hs_VS_il6_hd

Seurat::VlnPlot(subsobj, group.by = "sample_type", pt.size = 0.3,
                features = "IL6", cols = sample_type_colors) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")
```

Violin plot of TNF in macrophages :

```{r fig_ic_mac_tnf, fig.width = 2, fig.height = 2.5}
tnf_hs = subsobj@assays$RNA@data["TNF", subsobj$sample_type == "HS"]
tnf_hd = subsobj@assays$RNA@data["TNF", subsobj$sample_type == "HD"]
tnf_hs_VS_tnf_hd = stats::t.test(tnf_hs, tnf_hd)
tnf_hs_VS_tnf_hd

Seurat::VlnPlot(subsobj, group.by = "sample_type", pt.size = 0.3,
                features = "TNF", cols = sample_type_colors) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")
```

Split by sample :

```{r fig_ic_mac_tnf_split, fig.width = 6, fig.height = 4}
Seurat::VlnPlot(subsobj, group.by = "sample_identifier",
                features = "TNF", cols = sample_info$color) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")
```


Violin plot of GZMA in CD4 T cells :

```{r fig_ic_t4_gzma, fig.width = 2, fig.height = 2.5}
subsobj = subset(sobj_ic, seurat_clusters %in% c(0,10))
table(subsobj$sample_type)

gzma_hs = subsobj@assays$RNA@data["GZMA", subsobj$sample_type == "HS"]
gzma_hd = subsobj@assays$RNA@data["GZMA", subsobj$sample_type == "HD"]
gzma_hs_VS_gzma_hd = stats::t.test(gzma_hs, gzma_hd)
gzma_hs_VS_gzma_hd

Seurat::VlnPlot(subsobj, group.by = "sample_type", pt.size = 0.3,
                features = "GZMA", cols = sample_type_colors) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")
```

Violin plot of IFNG in CD4 T cells :

```{r fig_ic_t4_ifng, fig.width = 2, fig.height = 2.5}
ifng_hs = subsobj@assays$RNA@data["IFNG", subsobj$sample_type == "HS"]
ifng_hd = subsobj@assays$RNA@data["IFNG", subsobj$sample_type == "HD"]
ifng_hs_VS_ifng_hd = stats::t.test(ifng_hs, ifng_hd)
ifng_hs_VS_ifng_hd

Seurat::VlnPlot(subsobj, group.by = "sample_type", pt.size = 0.3,
                features = "IFNG", cols = sample_type_colors) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")
```
Violin plot of IL17A in CD4 T cells :

```{r fig_ic_t4_IL17, fig.width = 2, fig.height = 2.5}
IL17_hs = subsobj@assays$RNA@data["IL17A", subsobj$sample_type == "HS"]
IL17_hd = subsobj@assays$RNA@data["IL17A", subsobj$sample_type == "HD"]
IL17_hs_VS_IL17_hd = stats::t.test(IL17_hs, IL17_hd)
IL17_hs_VS_IL17_hd

Seurat::VlnPlot(subsobj, group.by = "sample_type", pt.size = 0.3,
                features = "IL17RE", cols = sample_type_colors) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")
```

We represent some genes split by sample type :

```{r fig_ic_gene_split, fig.width = 6, fig.height = 3}
plot_list = lapply(c("IL1B", "GZMA", "IFNG", "IL17A", "TNF", "IL6"), FUN = function(one_gene) {
  p = aquarius::plot_split_dimred(sobj_ic,
                                  reduction = name2D,
                                  split_by = "sample_type",
                                  color_by = one_gene,
                                  color_palette = c("gray70", "#FDBB84", "#EF6548", "#7F0000", "black"),
                                  main_pt_size = 0.6,
                                  bg_pt_size = 0.6,
                                  order = TRUE,
                                  bg_color = "gray95")
  p = patchwork::wrap_plots(p, nrow = 1) +
    patchwork::plot_layout(guides = "collect") +
    ggplot2::theme(legend.position = "right") &
    ggplot2::theme(plot.subtitle = element_blank())
  return(p)
})

plot_list
```

Barplot by cluster type :

```{r fig_ic_barplot, fig.width = 4, fig.height = 4}
quantif = table(sobj_ic$sample_identifier) %>%
  as.data.frame.table() %>%
  `colnames<-`(c("Sample", "nb_cells"))

aquarius::plot_barplot(df = table(sobj_ic$sample_identifier,
                                  sobj_ic$cluster_type) %>%
                         as.data.frame.table() %>%
                         `colnames<-`(c("Sample", "Cell Type", "Number")),
                       x = "Sample", y = "Number", fill = "Cell Type",
                       position = position_stack()) +
  ggplot2::geom_label(data = quantif, inherit.aes = FALSE,
                      aes(x = .data$Sample, y = 50 + .data$nb_cells, label = .data$nb_cells),
                      label.size = 0, size = 5) +
  ggplot2::scale_fill_manual(values = unlist(color_markers),
                             breaks = names(color_markers),
                             name = "Cell Type") +
  ggplot2::scale_y_continuous(limits = c(0, 100 + max(quantif$nb_cells)),
                              expand = ggplot2::expansion(add = c(0, 0.05))) +
  ggplot2::theme(axis.title.y = element_blank(),
                 axis.line.x = element_line(colour = "lightgray"),
                 text = element_text(size = 15),
                 legend.position = "none")
```

Heatmap for macrophages :

```{r fig_ic_heatmap_macrophages, fig.width = 5, fig.height = 5.2, class.source = "fold-hide"}
subsobj = subset(sobj_ic, cluster_type == "macrophages")
features_oi = c("IL1B", "TNF",
                "HLA-DQA2", "HLA-DPA1", "HLA-DRB5",
                "HLA-A", "HLA-C", "B2M",
                "C1QA", "C1QB", "C1QC")

# Matrix
mat_expr = Seurat::GetAssayData(subsobj)
mat_expr = mat_expr[features_oi, ]
mat_expr = Matrix::t(mat_expr)
mat_expr = dynutils::scale_quantile(mat_expr) # between 0 and 1
mat_expr = Matrix::t(mat_expr)
dim(mat_expr) # genes x cells
## Colors
list_colors = list()

# Heatmap
list_colors[["expression"]] = rev(RColorBrewer::brewer.pal(name = "RdBu", n = 9))

# Sample annotation (top annotation)
list_colors[["sample_type"]] = sample_type_colors
list_colors[["sample_identifier"]] = setNames(nm = sample_info$sample_identifier,
                                              sample_info$color)
# Cells order
column_order = subsobj@meta.data %>%
  dplyr::arrange(sample_type, sample_identifier) %>%
  rownames()
column_order = match(column_order, rownames(subsobj@meta.data))

# Heatmap
ha_top = HeatmapAnnotation(sample_type = subsobj$sample_type,
                           sample_identifier = subsobj$sample_identifier,
                           col = list(sample_type = list_colors[["sample_type"]],
                                      sample_identifier = list_colors[["sample_identifier"]]))

# Heatmap
ht = Heatmap(as.matrix(mat_expr),
             heatmap_legend_param = list(title = "Expression", at = c(0, 1), 
                                         labels = c("low", "high")),
             col = list_colors[["expression"]],
             top_annotation = ha_top,
             show_column_names = FALSE,
             column_order = column_order,
             column_gap = unit(2, "mm"),
             cluster_rows = FALSE,
             row_title = NULL,
             row_names_gp = grid::gpar(fontsize = 14, fontface = "plain"),
             use_raster = FALSE,
             show_heatmap_legend = TRUE,
             border = TRUE)

ComplexHeatmap::draw(ht,
                     merge_legend = TRUE,
                     heatmap_legend_side = "bottom",
                     annotation_legend_side = "bottom")
```

Heatmap for CD4 T cells :

```{r fig_ic_heatmap_cd4, fig.width = 6, fig.height = 4, class.source = "fold-hide"}
subsobj = subset(sobj_ic, cluster_type == "CD4 T cells")
features_oi = c("GZMA", "KLRB1", "BTG1", "ZFP36", "NFKBIA", "TXNIP", "CXCR4", "IFNG", "IL17A")

# Matrix
mat_expr = Seurat::GetAssayData(subsobj)
mat_expr = mat_expr[features_oi, ]
mat_expr = Matrix::t(mat_expr)
mat_expr = dynutils::scale_quantile(mat_expr) # between 0 and 1
mat_expr = Matrix::t(mat_expr)
dim(mat_expr) # genes x cells
## Colors
list_colors = list()

# Heatmap
list_colors[["expression"]] = rev(RColorBrewer::brewer.pal(name = "RdBu", n = 9))

# Sample annotation (top annotation)
list_colors[["sample_type"]] = sample_type_colors
list_colors[["sample_identifier"]] = setNames(nm = sample_info$sample_identifier,
                                              sample_info$color)
# Cells order
column_order = subsobj@meta.data %>%
  dplyr::arrange(sample_type, sample_identifier) %>%
  rownames()
column_order = match(column_order, rownames(subsobj@meta.data))

# Heatmap
ha_top = HeatmapAnnotation(sample_type = subsobj$sample_type,
                           sample_identifier = subsobj$sample_identifier,
                           col = list(sample_type = list_colors[["sample_type"]],
                                      sample_identifier = list_colors[["sample_identifier"]]))

# Heatmap
ht = Heatmap(as.matrix(mat_expr),
             heatmap_legend_param = list(title = "Expression", at = c(0, 1), 
                                         labels = c("low", "high")),
             col = list_colors[["expression"]],
             top_annotation = ha_top,
             show_column_names = FALSE,
             column_order = column_order,
             column_gap = unit(2, "mm"),
             cluster_rows = FALSE,
             row_title = NULL,
             row_names_gp = grid::gpar(fontsize = 14, fontface = "plain"),
             use_raster = FALSE,
             show_heatmap_legend = TRUE,
             border = TRUE)

ComplexHeatmap::draw(ht,
                     merge_legend = TRUE,
                     heatmap_legend_side = "left",
                     annotation_legend_side = "left")
```


# HFSC

## Settings

We load the HFSCs dataset :

```{r sobj_hfsc}
sobj_hfsc = readRDS(paste0(data_dir, "/4_zoom/2_zoom_hfsc/hfsc_sobj.rds"))
sobj_hfsc
```


This is the projection name to visualize cells :

```{r sobj_name2D_hfsc}
name2D = "harmony_24_tsne"
```

To represent results from differential expression, we load the analyses results :

```{r list_results_hfsc}
list_results = readRDS(paste0(data_dir, "/4_zoom/2_zoom_hfsc/hfsc_list_results.rds"))

lapply(list_results, FUN = names)
```

## Figures

HFSCs on the full atlas :

```{r fig_hfsc_location, fig.width = 2, fig.height = 2}
sobj$is_hfsc = (colnames(sobj) %in% colnames(sobj_hfsc))

Seurat::DimPlot(sobj, reduction = name2D_atlas, pt.size = 0.000001,
                group.by = "is_hfsc", order = "TRUE") +
  ggplot2::scale_color_manual(values = c(color_markers[["HFSC"]], bg_color),
                              breaks = c(TRUE, FALSE)) +
  ggplot2::labs(title = "HFSCs",
                subtitle = paste0(ncol(sobj_hfsc), " cells")) +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5, face = "bold"),
                 plot.subtitle = element_text(hjust = 0.5)) +
  Seurat::NoAxes() + Seurat::NoLegend()
```

KRT15 expression :

```{r fig_hfsc_krt15, fig.width = 2, fig.height = 2}
Seurat::FeaturePlot(sobj, reduction = name2D_atlas, pt.size = 0.000001,
                    features = "KRT15") +
  ggplot2::scale_color_gradientn(colors = aquarius::color_gene) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() + Seurat::NoLegend()
```

Genes of interest :

```{r fig_hfsc_genes, fig.width = 4, fig.height = 4}
genes = c("TGFB2", "ANGPTL7", "FGF18", "MGP", "EPCAM", "KRT75", "NOTCH3", "PTHLH")

plot_list = lapply(c(1:length(genes)), FUN = function(gene_id) {
  gene = genes[[gene_id]]
  
  sobj_hfsc$my_gene = Seurat::FetchData(sobj_hfsc, gene)[, 1] %>%
    aquarius::run_rescale(., new_min = 0, new_max = 10)
  
  Seurat::FeaturePlot(sobj_hfsc, features = "my_gene", reduction = name2D) +
    ggplot2::scale_color_gradientn(colors = aquarius::color_gene,
                                   breaks = seq(0, 10, by = 2.5),
                                   labels = c("min", rep("", 3), "max")) +
    ggplot2::labs(title = gene) +
    ggplot2::theme(aspect.ratio = 1,
                   plot.title = element_text(hjust = 0.5, size = 17),
                   plot.subtitle = element_text(hjust = 0.5, size = 15),
                   legend.text = element_text(size = 15),
                   legend.position = "none") +
    Seurat::NoAxes()
})

plot_list
```

Project name :

```{r fig_hfsc_project_name, fig.width = 4, fig.height = 4}
# Random order
set.seed(1234)
rnd_order = sample(colnames(sobj_hfsc), replace = FALSE, size = ncol(sobj_hfsc))

# Extract coordinates
cells_coord = sobj_hfsc@reductions[[name2D]]@cell.embeddings %>%
  as.data.frame() %>%
  `colnames<-`(c("Dim1", "Dim2"))
cells_coord$project_name = sobj_hfsc$project_name
cells_coord = cells_coord[(rnd_order), ]

# Plot
ggplot2::ggplot(cells_coord, aes(x = Dim1, y = Dim2, col = project_name)) +
  ggplot2::geom_point(size = 1.2) +
  ggplot2::scale_color_manual(values = sample_info$color,
                              breaks = sample_info$project_name) +
  ggplot2::theme_void() +
  ggplot2::theme(aspect.ratio = 1,
                 legend.position = "none")
```

Cluster :

```{r fig_hfsc_clusters, fig.width = 4, fig.height = 4}
Seurat::DimPlot(sobj_hfsc, reduction = name2D, pt.size = 1,
                group.by = "seurat_clusters", cols = grey_palette,
                label = TRUE, label.size = 7) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() +
  Seurat::NoLegend()
```

Heatmap with proportions :

```{r fig_hfsc_heatmap, fig.width = 5.5, fig.height = 5.5, class.source = "fold-hide"}
cluster_markers = c("TGFB2", "ANGPTL7", "EPCAM", "KRT75", "NOTCH3", "PTHLH")

## Bottom annotation : gene expression by cluster
ht_annot = Seurat::FetchData(sobj_hfsc, slot = "data", vars = cluster_markers) %>%
  as.data.frame()
ht_annot$clusters = sobj_hfsc$seurat_clusters
ht_annot = ht_annot %>%
  dplyr::group_by(clusters) %>%
  dplyr::summarise_all(funs('mean' = mean)) %>%
  as.data.frame() %>%
  dplyr::select(-clusters) %>%
  `colnames<-`(c(cluster_markers))

ha_bottom = ComplexHeatmap::HeatmapAnnotation(df = ht_annot,
                                              which = "column",
                                              show_legend = TRUE,
                                              col = setNames(nm = cluster_markers,
                                                             lapply(cluster_markers, FUN = color_fun)),
                                              annotation_name_side = "left")

## Right annotation : number of cells by dataset
ht_annot = table(sobj_hfsc$sample_identifier) %>%
  as.data.frame.table() %>%
  `colnames<-`(c("sample_identifier", "nb_cells")) %>%
  `rownames<-`(.$sample_identifier) %>%
  dplyr::select(-sample_identifier)

ha_right = ComplexHeatmap::HeatmapAnnotation(
  df = ht_annot,
  which = "row",
  show_legend = TRUE,
  annotation_name_side = "bottom",
  col = list(nb_cells  = circlize::colorRamp2(colors = RColorBrewer::brewer.pal(name = "Greys", n = 9),
                                              breaks = seq(from = range(ht_annot$nb_cells)[1],
                                                           to = range(ht_annot$nb_cells)[2],
                                                           length.out = 9))))

## Heatmap
ht = aquarius::plot_prop_heatmap(df = sobj_hfsc@meta.data[, c("sample_identifier", "seurat_clusters")],
                                 bottom_annotation = ha_bottom,
                                 # right_annotation = ha_right,
                                 cluster_rows = TRUE,
                                 column_names_centered = TRUE,
                                 prop_margin = 1,
                                 row_names_gp = grid::gpar(names = sample_info$sample_identifier,
                                                           col = sample_info$color,
                                                           fontface = "bold"),
                                 row_title = "Sample",
                                 column_title = "Cluster")

ComplexHeatmap::draw(ht,
                     merge_legend = TRUE,
                     heatmap_legend_side = "bottom")
```

Heatmap for cluster 0 and 8 :

```{r fig_hfsc_heatmap_cluster08, fig.width = 5.5, fig.height = 10, class.source = "fold-hide"}
subsobj = subset(sobj_hfsc, seurat_clusters %in% c(0,8))

features_oi = rownames(list_results$cluster_0_8)
features_oi = features_oi[!grepl(features_oi, pattern = "^RP")]

# Matrix
mat_expr = Seurat::GetAssayData(subsobj)
mat_expr = mat_expr[features_oi, ]
mat_expr = Matrix::t(mat_expr)
mat_expr = cbind(mat_expr, subsobj$percent.mt)
colnames(mat_expr)[ncol(mat_expr)] = "percent.rb"
mat_expr = dynutils::scale_quantile(mat_expr) # between 0 and 1
mat_expr = Matrix::t(mat_expr)
dim(mat_expr) # genes x cells
## Colors
list_colors = list()

# Heatmap
list_colors[["expression"]] = rev(RColorBrewer::brewer.pal(name = "RdBu", n = 9))

# Sample annotation (top annotation)
list_colors[["sample_type"]] = sample_type_colors
list_colors[["sample_identifier"]] = setNames(nm = sample_info$sample_identifier,
                                              sample_info$color)
# list_colors[["seurat_clusters"]] = setNames(aquarius::gg_color_hue(length(levels(subsobj$seurat_clusters))),
#                                             nm = levels(subsobj$seurat_clusters))
# Cells order
column_order = subsobj@meta.data %>%
  dplyr::arrange(sample_type, sample_identifier) %>%
  rownames()
column_order = match(column_order, rownames(subsobj@meta.data))

# Heatmap
ha_top = HeatmapAnnotation(sample_type = subsobj$sample_type,
                           sample_identifier = subsobj$sample_identifier,
                           # clusters = subsobj$seurat_clusters,
                           col = list(sample_type = list_colors[["sample_type"]],
                                      sample_identifier = list_colors[["sample_identifier"]]
                                      # clusters = list_colors[["seurat_clusters"]]
                           ))


# g1 : REACTOME_CYTOKINE_SIGNALING_IN_IMMUNE_SYSTEM
# g2 : GOBP_APOPTOTIC_PROCESS
g1_genes = c("B2M", "HLA-C", "HLA-A", "MIF", "PPIA", "JUNB", "IFITM3")
g2_genes = c("Jun", "ATF3", "BTG2", "RHOB", "NFKBIA", "SGK1", "KLF9",
             "CAV1", "DDIT4", "PDK4", "TXNIP", "RNF1152", "TLE1")
ha_right = data.frame(genes =  c(features_oi, "percent.rb"), rownames = c(features_oi, "percent.rb"))
ha_right$group = case_when(ha_right$genes %in% g1_genes ~ "REACTOME_CYTOKINE_SIGNALING_IN_IMMUNE_SYSTEM",
                           ha_right$genes %in% g2_genes ~ "GOBP_APOPTOTIC_PROCESS",
                           TRUE ~ "others")

list_colors[["group"]] = setNames(
  nm = c("REACTOME_CYTOKINE_SIGNALING_IN_IMMUNE_SYSTEM", "GOBP_APOPTOTIC_PROCESS", "others"),
  c("red", "black", "gray90"))

ha_right = HeatmapAnnotation(group = ha_right$group,
                             col = list(group = list_colors[["group"]]),
                             which = "row",
                             show_annotation_name = FALSE,
                             show_legend = TRUE)

# Heatmap
ht = Heatmap(as.matrix(mat_expr),
             heatmap_legend_param = list(title = "Expression", at = c(0, 1), 
                                         labels = c("low", "high")),
             col = list_colors[["expression"]],
             top_annotation = ha_top,
             right_annotation = ha_right,
             show_column_names = FALSE,
             column_order = column_order,
             column_gap = unit(2, "mm"),
             cluster_rows = FALSE,
             row_title = NULL,
             row_names_gp = grid::gpar(fontsize = 10, fontface = "plain"),
             use_raster = FALSE,
             show_heatmap_legend = TRUE,
             border = TRUE)

ComplexHeatmap::draw(ht,
                     merge_legend = TRUE,
                     heatmap_legend_side = "bottom",
                     annotation_legend_side = "bottom")
```

Violin plot of IFITM3 :

```{r fig_hfsc_ifitm3, fig.width = 3, fig.height = 4}
table(subsobj$sample_type)

ifitm3_hs = subsobj@assays$RNA@data["IFITM3", subsobj$sample_type == "HS"]
ifitm3_hd = subsobj@assays$RNA@data["IFITM3", subsobj$sample_type == "HD"]
ifitm3_hs_VS_ifitm3_hd = stats::t.test(ifitm3_hs, ifitm3_hd)
ifitm3_hs_VS_ifitm3_hd

Seurat::VlnPlot(subsobj, group.by = "sample_type",
                features = "IFITM3", cols = sample_type_colors) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")
```

Split by sample :

```{r fig_hfsc_ifitm3_split, fig.width = 6, fig.height = 4}
Seurat::VlnPlot(subsobj, group.by = "sample_identifier",
                features = "IFITM3", cols = sample_info$color) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")
```


Violin plot of DDIT4 :

```{r fig_hfsc_ddit4, fig.width = 3, fig.height = 4}
table(subsobj$sample_type)

DDIT4_hs = subsobj@assays$RNA@data["DDIT4", subsobj$sample_type == "HS"]
DDIT4_hd = subsobj@assays$RNA@data["DDIT4", subsobj$sample_type == "HD"]
DDIT4_hs_VS_DDIT4_hd = stats::t.test(DDIT4_hs, DDIT4_hd)
DDIT4_hs_VS_DDIT4_hd

Seurat::VlnPlot(subsobj, group.by = "sample_type",
                features = "DDIT4", cols = sample_type_colors) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")
```

Split by sample :

```{r fig_hfsc_ddit4_split, fig.width = 6, fig.height = 4}
Seurat::VlnPlot(subsobj, group.by = "sample_identifier",
                features = "DDIT4", cols = sample_info$color) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")
```

We represent some genes split by sample type :

```{r fig_hfsc_gene_split, fig.width = 12, fig.height = 4}
plot_list = lapply(c("DDIT4", "IFITM3"), FUN = function(one_gene) {
  p = aquarius::plot_split_dimred(sobj_hfsc,
                                  reduction = name2D,
                                  split_by = "sample_type",
                                  color_by = one_gene,
                                  color_palette = c("gray70", "#FDBB84", "#EF6548", "#7F0000", "black"),
                                  main_pt_size = 0.6,
                                  bg_pt_size = 0.6,
                                  order = TRUE,
                                  bg_color = "gray95")
  p = patchwork::wrap_plots(p, nrow = 1) +
    patchwork::plot_layout(guides = "collect") +
    ggplot2::theme(legend.position = "right") &
    ggplot2::theme(plot.subtitle = element_blank())
  return(p)
})

patchwork::wrap_plots(plot_list, ncol = 2)
```


Barplot with number of HFSCs and total number of cells :

```{r fig_hfsc_barplot, fig.width = 5.5, fig.height = 4.5}
quantif = dplyr::left_join(
  x = table(sobj$sample_identifier) %>%
    as.data.frame.table() %>%
    `colnames<-`(c("Sample", "nb_cells")),
  y = table(sobj_hfsc$sample_identifier) %>%
    as.data.frame.table() %>%
    `colnames<-`(c("Sample", "nb_hfsc")),
  by = "Sample") %>%
  dplyr::mutate(prop_hfsc = round(100*nb_hfsc / nb_cells, 2))

quantif_to_plot = rbind.data.frame(
  data.frame(Sample = quantif$Sample,
             nb_cells = quantif$nb_cells - quantif$nb_hfsc,
             cell_type = "others",
             stringsAsFactors = FALSE),
  data.frame(Sample = quantif$Sample,
             nb_cells = quantif$nb_hfsc,
             cell_type = "hfsc",
             stringsAsFactors = FALSE)) %>%
  dplyr::mutate(cell_type = factor(cell_type, levels = c("others", "hfsc")))

aquarius::plot_barplot(df = quantif_to_plot,
                       x = "Sample", y = "nb_cells", fill = "cell_type",
                       position = position_fill()) +
  ggplot2::geom_label(data = quantif, inherit.aes = FALSE,
                      aes(x = .data$Sample, y = 0.05+.data$prop_hfsc/100, label = .data$nb_hfsc),
                      label.size = 0, size = 5, fill = NA) +
  ggplot2::scale_fill_manual(values = c("gray90", color_markers[["HFSC"]]),
                             breaks = c("others", "hfsc"),
                             name = "Cell Type") +
  ggplot2::scale_y_continuous(breaks = seq(0, 1, by = 0.25),
                              labels = paste0(seq(0, 100, by = 25), sep = " %"),
                              expand = ggplot2::expansion(add = c(0, 0.05))) +
  ggplot2::theme(axis.title.y = element_blank(),
                 axis.line.x = element_line(colour = "lightgray"),
                 text = element_text(size = 15),
                 legend.position = "none")
```

# IBL and ORS

## Settings

We load the IBL + ORS dataset :

```{r sobj_iblors}
sobj_iblors = readRDS(paste0(data_dir, "/4_zoom/3_zoom_iblmors/iblmors_sobj.rds"))
sobj_iblors
```


This is the projection name to visualize cells :

```{r sobj_name2D_iblors}
name2D = "harmony_20_tsne"
```

To represent results from differential expression, we load the analyses results :

```{r list_results_iblors}
list_results = readRDS(paste0(data_dir, "/4_zoom/3_zoom_iblmors/iblmors_list_results.rds"))

lapply(list_results, FUN = names)
```

## Preparation

We defined cluster type :

```{r cluster_type_iblors, fig.width = 7, fig.height = 5}
cluster_type = table(sobj_iblors$cell_type, sobj_iblors$seurat_clusters) %>%
  prop.table(., margin = 2) %>%
  apply(., 2, which.max)
cluster_type = setNames(nm = names(cluster_type),
                        levels(sobj_iblors$cell_type)[cluster_type])

sobj_iblors$cluster_type = cluster_type[sobj_iblors$seurat_clusters]
sobj_iblors$cluster_type = factor(sobj_iblors$cluster_type,
                                  levels = c("IBL", "ORS"))
```

## Figures

IBL + ORS on the full atlas :

```{r fig_iblors_location, fig.width = 2, fig.height = 2}
sobj$cell_bc = colnames(sobj)
sobj_iblors$cell_bc = colnames(sobj_iblors)
sobj$is_iblors = dplyr::left_join(sobj@meta.data[, c("cell_bc", "percent.mt")],
                                  sobj_iblors@meta.data[, c("cell_bc", "cluster_type")],
                                  by = "cell_bc")[, "cluster_type"]

Seurat::DimPlot(sobj, reduction = name2D_atlas, pt.size = 0.000001,
                group.by = "is_iblors", order = "TRUE") +
  ggplot2::scale_color_manual(values = c(color_markers[c("IBL", "ORS")], bg_color),
                              breaks = c("IBL", "ORS", NA), na.value = bg_color) +
  ggplot2::labs(title = "IBL + ORS",
                subtitle = paste0(ncol(sobj_iblors), " cells")) +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5, face = "bold"),
                 plot.subtitle = element_text(hjust = 0.5)) +
  Seurat::NoAxes() + Seurat::NoLegend()
```


Project name :

```{r fig_iblors_project_name, fig.width = 4, fig.height = 4}
# Random order
set.seed(1234)
rnd_order = sample(colnames(sobj_iblors), replace = FALSE, size = ncol(sobj_iblors))

# Extract coordinates
cells_coord = sobj_iblors@reductions[[name2D]]@cell.embeddings %>%
  as.data.frame() %>%
  `colnames<-`(c("Dim1", "Dim2"))
cells_coord$project_name = sobj_iblors$project_name
cells_coord = cells_coord[(rnd_order), ]

# Plot
ggplot2::ggplot(cells_coord, aes(x = Dim1, y = Dim2, col = project_name)) +
  ggplot2::geom_point(size = 1.2) +
  ggplot2::scale_color_manual(values = sample_info$color,
                              breaks = sample_info$project_name) +
  ggplot2::theme_void() +
  ggplot2::theme(aspect.ratio = 1,
                 legend.position = "none")
```

Cluster :

```{r fig_iblors_clusters, fig.width = 4, fig.height = 4}
Seurat::DimPlot(sobj_iblors, reduction = name2D, pt.size = 1,
                group.by = "seurat_clusters", cols = grey_palette,
                label = TRUE, label.size = 8) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() +
  Seurat::NoLegend()
```

Cluster type :

```{r fig_iblors_cluster_type, fig.width = 4, fig.height = 4}
Seurat::DimPlot(sobj_iblors, reduction = name2D, pt.size = 1,
                group.by = "cluster_type", cols = color_markers) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() +
  Seurat::NoLegend()
```

Cluster type split by sample type :

```{r fig_iblors_cluster_type_split, fig.width = 8, fig.height = 4}
plot_list = aquarius::plot_split_dimred(sobj_iblors, reduction = name2D,
                                        group_by = "cluster_type",
                                        group_color = color_markers,
                                        split_by = "sample_type",
                                        bg_pt_size = 1, main_pt_size = 1,
                                        bg_color = bg_color)

patchwork::wrap_plots(plot_list) &
  Seurat::NoLegend()
```


Barplot by cluster family :

```{r fig_iblors_barplot, fig.width = 5.5, fig.height = 4.5}
sobj_iblors$cluster_type_sep5 = ifelse(sobj_iblors$seurat_clusters == 5,
                                       yes = "ORS_5",
                                       no = as.character(sobj_iblors$cluster_type)) %>%
  as.factor()

quantif = table(sobj_iblors$sample_identifier) %>%
  as.data.frame.table() %>%
  `colnames<-`(c("Sample", "nb_cells"))

quantif_to_plot = table(sobj_iblors$sample_identifier,
                        sobj_iblors$cluster_type_sep5) %>%
  as.data.frame.table() %>%
  `colnames<-`(c("Sample", "CellType", "Number")) %>%
  dplyr::mutate(Style = ifelse(CellType == "ORS_5", yes = "IL1R2+", no = "IL1R2-")) %>%
  dplyr::mutate(Style = factor(Style, levels = c("IL1R2-", "IL1R2+"))) %>%
  dplyr::mutate(CellType = ifelse(CellType == "ORS_5", yes = "ORS", no = as.character(CellType))) %>%
  `colnames<-`(c("Sample", "Cell Type", "Number", "IL1R2 status"))

aquarius::plot_barplot(df = quantif_to_plot,
                       x = "Sample", y = "Number",
                       fill = "Cell Type", pattern = "IL1R2 status",
                       position = position_fill()) +
  ggplot2::geom_label(data = quantif, inherit.aes = FALSE,
                      aes(x = .data$Sample, y = 1.05, label = .data$nb_cells),
                      label.size = 0, size = 5) +
  ggplot2::scale_fill_manual(values = unlist(color_markers[levels(sobj_iblors$cluster_type)]),
                             breaks = names(color_markers[levels(sobj_iblors$cluster_type)]),
                             name = "Cell Type") +
  ggplot2::scale_y_continuous(breaks = seq(0, 1, by = 0.25),
                              labels = paste0(seq(0, 100, by = 25), sep = " %"),
                              expand = ggplot2::expansion(add = c(0, 0.05))) +
  ggplot2::theme(axis.title.y = element_blank(),
                 axis.line.x = element_line(colour = "lightgray"),
                 text = element_text(size = 15),
                 legend.position = "right")
```


DE genes between IBL and ORS :

```{r fig_iblors_de_pop, fig.width = 6, fig.height = 6}
mark = list_results$IBL_vs_ORS$mark
mark$gene_name = rownames(mark)
mark_label = rbind(
  # up-regulated in IBL
  mark %>% dplyr::top_n(., n = 20, wt = avg_logFC),
  # up-regulated in ORS
  mark %>% dplyr::top_n(., n = 20, wt = -avg_logFC),
  # representative and selective for IBL
  mark %>% dplyr::top_n(., n = 20, wt = (pct.1 - pct.2)),
  # representative and selective for ORS
  mark %>% dplyr::top_n(., n = 20, wt = -(pct.1 - pct.2))) %>%
  dplyr::distinct()
mark_label = mark_label[!grepl(rownames(mark_label), pattern = "^MT"), ]

avg_logFC_range = setNames(c(min(mark_label$avg_logFC), -1, 0, 1, max(mark_label$avg_logFC)),
                           nm = c("dodgerblue4", "dodgerblue3", "#B7B7B7", "firebrick3", "firebrick4"))


ggplot2::ggplot(mark, aes(x = pct.1, y = pct.2, col = avg_logFC)) +
  ggplot2::geom_abline(slope = 1, intercept = 0, lty = 2) +
  ggplot2::geom_point() +
  ggrepel::geom_label_repel(data = mark_label, max.overlaps = Inf,
                            aes(x = pct.1, y = pct.2, label = gene_name),
                            col = "black", fill = NA, size = 3.5, label.size = NA) +
  ggplot2::labs(x = "Enriched in IBL",
                y = "Enriched in ORS") +
  ggplot2::scale_color_gradientn(colors = names(avg_logFC_range),
                                 values = scales::rescale(unname(avg_logFC_range))) +
  ggplot2::theme_classic() +
  ggplot2::theme(aspect.ratio = 1)
```

GSEA plot :

```{r fig_iblors_keratinization, fig.width = 6, fig.height = 4}
the_gs_name = "REACTOME_KERATINIZATION" 
the_content = list_results$IBL_vs_ORS$gsea@result %>%
  dplyr::filter(ID == the_gs_name)
the_subtitle = paste0("\nNES : ", round(the_content$NES, 2),
                      " | pvalue : ", round(the_content$pvalue, 4),
                      " | set size : ", the_content$setSize, " genes")

enrichplot::gseaplot2(x = list_results$IBL_vs_ORS$gsea,
                      geneSetID = the_gs_name) +
  ggplot2::labs(title = the_gs_name,
                subtitle = the_subtitle) +
  ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold",
                                           margin = ggplot2::margin(3, 3, 5, 3)),
                 plot.subtitle = ggtext::element_markdown(hjust = 0.5,
                                                          size = 10))
```

```{r fig_iblors_ifna, fig.width = 6, fig.height = 4}
the_gs_name = "HALLMARK_INTERFERON_GAMMA_RESPONSE" 
the_content = list_results$IBL_vs_ORS$gsea@result %>%
  dplyr::filter(ID == the_gs_name)
the_subtitle = paste0("\nNES : ", round(the_content$NES, 2),
                      " | pvalue : ", round(the_content$pvalue, 4),
                      " | set size : ", the_content$setSize, " genes")

enrichplot::gseaplot2(x = list_results$IBL_vs_ORS$gsea,
                      geneSetID = the_gs_name) +
  ggplot2::labs(title = the_gs_name,
                subtitle = the_subtitle) +
  ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold",
                                           margin = ggplot2::margin(3, 3, 5, 3)),
                 plot.subtitle = ggtext::element_markdown(hjust = 0.5,
                                                          size = 10))
```

Score for both gene sets, in all cells :

```{r fig_iblors_kera_score, fig.width = 6, fig.height = 4}
gene_sets = aquarius::get_gene_sets(species = "Homo sapiens")
the_gs_name = "REACTOME_KERATINIZATION" 
the_gs_content = gene_sets$gene_sets_full %>%
  dplyr::filter(gs_name == the_gs_name) %>%
  dplyr::pull(gene_symbol) %>%
  unlist()

sobj_iblors$score_kera = Seurat::AddModuleScore(sobj_iblors,
                                                features = list(the_gs_content))$Cluster1

Seurat::VlnPlot(sobj_iblors, features = "score_kera", pt.size = 0.05,
                split.by = "sample_type", group.by = "cluster_type",
                cols = rev(sample_type_colors)) +
  ggplot2::labs(title = the_gs_name) +
  ggplot2::theme(axis.title.x = element_blank())
```

```{r fig_iblors_ifna_score, fig.width = 6, fig.height = 4}
the_gs_name = "HALLMARK_INTERFERON_GAMMA_RESPONSE" 
the_gs_content = gene_sets$gene_sets_full %>%
  dplyr::filter(gs_name == the_gs_name) %>%
  dplyr::pull(gene_symbol) %>%
  unlist()

sobj_iblors$score_ifna = Seurat::AddModuleScore(sobj_iblors,
                                                features = list(the_gs_content))$Cluster1

Seurat::VlnPlot(sobj_iblors, features = "score_ifna", pt.size = 0.05,
                split.by = "sample_type", group.by = "cluster_type",
                cols = rev(sample_type_colors)) +
  ggplot2::labs(title = the_gs_name) +
  ggplot2::theme(axis.title.x = element_blank())
```


Violin plot for IBL :

```{r fig_iblors_ibl, fig.width = 12, fig.height = 2.5}
subsobj = subset(sobj_iblors, cluster_type == "IBL")

Seurat::VlnPlot(subsobj, group.by = "sample_type", pt.size = 0.05,
                features = c("DUSP1", "DDIT4", "MIF", "LGALS7", "ARF5", "S100A9"),
                cols = sample_type_colors, ncol = 6) &
  ggplot2::theme(axis.title.x = element_blank(),
                 axis.title.y = element_blank(),
                 legend.position = "none")
```

Violin plot for ORS :

```{r fig_iblors_ors, fig.width = 12, fig.height = 2.5}
subsobj = subset(sobj_iblors, cluster_type == "ORS")

Seurat::VlnPlot(subsobj, group.by = "sample_type", pt.size = 0.05,
                features = c("DUSP1", "KLF6", "CLDN1", "CTGF",
                             "S100A9", "CCL2", "IFITM3", "IFI27"),
                cols = sample_type_colors, ncol = 8) &
  ggplot2::theme(axis.title.x = element_blank(),
                 axis.title.y = element_blank(),
                 legend.position = "none")
```

Split by sample :

```{r fig_iblors_ors_split, fig.width = 6, fig.height = 4}
Seurat::VlnPlot(subsobj, group.by = "sample_identifier",
                features = "IFI27", cols = sample_info$color) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")
```

Heatmap for cluster 5 vs other ORS :

```{r fig_iblors_heatmap_cluster5, fig.width = 12, fig.height = 19, class.source = "fold-hide"}
subsobj = subset(sobj_iblors, cluster_type == "ORS")

features_oi = c("YBX3", "TXNIP", "KRT14", "KRT15", "NEAT1",
                "FXYD3", "MT2A", "MT1E", "MT1X", "AQP3", "GLUL",
                # "HALLMARK_TNFA_SIGNALING_VIA_NFKB"
                "FOS", "JUNB", "DUSP1", "ZFP36", "NFKBIZ",
                "ATF3", "RHOB",  "ETS2", "IL18", "KLF4", "KLF6", "KLF9",
                "KLF3", "KLF5", "COL17A1", "THSD4", "WNT3", "WNT4", "SLPI", "PLAT",
                "LAMB4", "DCN", "SPINK5",
                "GSTM3", "ALDH3A1",  "LGALS7B", "SLC38A2", "EHF",  "CLEC2B",
                "IL20RB", "IL1R2", "IFI27", "CXCL14", "HLA-C", "GPSM2", "DAAM1",   "ID1",
                "RNASET2", "HOPX", "POU3F1", "SPRY1", "AR", "PDGFC",
                "WFDC2", "WFDC5", "TSC22D3", "FGFR3",  "LY6D", "IGFBP3", 
                # Other ORS
                "APOE", "CTSB", "CALD1", "SOX4",
                "STMN1", "LMO4", "CEBPB", "TMEM45A", "GPX2", "C1QTNF12", "GJB6",
                "KRT6A", "KRT17", "RBP1", "CALML3", "PTN", "DAPK2",
                "EGLN3", "FILIP1L", "ADGRL3", "FST", "EFNB2", "SEMA5A",
                "FGFR1", "EGR2", "CLDN1", "DEFB1", "CARD18", "MGST1")

# Matrix
mat_expr = Seurat::GetAssayData(subsobj)
mat_expr = mat_expr[features_oi, ]
mat_expr = Matrix::t(mat_expr)
mat_expr = dynutils::scale_quantile(mat_expr) # between 0 and 1
mat_expr = Matrix::t(mat_expr)
dim(mat_expr) # genes x cells

## Colors
list_colors = list()
list_colors[["expression"]] = rev(RColorBrewer::brewer.pal(name = "RdBu", n = 9))
list_colors[["sample_type"]] = sample_type_colors
list_colors[["sample_identifier"]] = setNames(nm = sample_info$sample_identifier,
                                              sample_info$color)
list_colors[["population"]] = setNames(nm = c("IL1R2+ ORS", "other ORS"),
                                       c("black", color_markers["ORS"]))
list_colors[["nFeature_RNA"]] = circlize::colorRamp2(breaks = seq(from = min(subsobj$nFeature_RNA),
                                                                  to = max(subsobj$nFeature_RNA),
                                                                  length.out = 9),
                                                     colors = RColorBrewer::brewer.pal(name = "Greys", n = 9))

# Cells order
column_order = subsobj@meta.data %>%
  dplyr::mutate(seurat_clusters = factor(seurat_clusters, levels = c(5, 3, 0, 1, 7))) %>%
  dplyr::arrange(sample_type, seurat_clusters, sample_identifier) %>%
  rownames()
column_order = match(column_order, rownames(subsobj@meta.data))

# Annotation
ha_top = HeatmapAnnotation(sample_type = subsobj$sample_type,
                           sample_identifier = subsobj$sample_identifier,
                           population = ifelse(subsobj$cluster_type_sep5 == "ORS",
                                               yes = "other ORS", no = "IL1R2+ ORS"),
                           col = list(sample_type = list_colors[["sample_type"]],
                                      sample_identifier = list_colors[["sample_identifier"]],
                                      population = list_colors[["population"]]))

ha_bottom = HeatmapAnnotation(nFeature_RNA = subsobj$nFeature_RNA,
                              col = list(nFeature_RNA = list_colors[["nFeature_RNA"]]))

# Heatmap
ht = Heatmap(as.matrix(mat_expr),
             heatmap_legend_param = list(title = "Expression", at = c(0, 1), 
                                         labels = c("low", "high")),
             col = list_colors[["expression"]],
             top_annotation = ha_top,
             bottom_annotation = ha_bottom,
             # Cell grouping
             column_split = subsobj$sample_type %>% as.character(),
             cluster_columns = FALSE,
             column_order = column_order,
             column_title = NULL,
             show_column_dend = FALSE,
             show_column_names = FALSE,
             # Genes
             cluster_rows = FALSE,
             row_names_gp = grid::gpar(fontsize = 14, fontface = "plain"),
             # Style
             use_raster = FALSE,
             show_heatmap_legend = TRUE,
             border = TRUE)

ComplexHeatmap::draw(ht,
                     merge_legend = TRUE,
                     heatmap_legend_side = "right",
                     annotation_legend_side = "right")
```

Genes of interest :

```{r fig_iblors_genes, fig.width = 2, fig.height = 2}
genes = c("KRT16", "COL17A1", "DST", "KRT6B", "IL1R2", "WNT3",
          "IFI27", "CXCL14", "IGFBP3", "KRT15", "CD200")

plot_list = lapply(c(1:length(genes)), FUN = function(gene_id) {
  gene = genes[[gene_id]]
  
  sobj_iblors$my_gene = Seurat::FetchData(sobj_iblors, gene)[, 1] %>%
    aquarius::run_rescale(., new_min = 0, new_max = 10)
  
  Seurat::FeaturePlot(sobj_iblors, features = "my_gene", reduction = name2D, pt.size = 0.25) +
    ggplot2::scale_color_gradientn(colors = aquarius::color_gene,
                                   breaks = seq(0, 10, by = 2.5),
                                   labels = c("min", rep("", 3), "max")) +
    ggplot2::labs(title = gene) +
    ggplot2::theme(aspect.ratio = 1,
                   plot.title = element_text(hjust = 0.5, size = 17),
                   plot.subtitle = element_text(hjust = 0.5, size = 15),
                   legend.text = element_text(size = 15),
                   legend.position = "none") +
    Seurat::NoAxes()
})

plot_list
```


# HFSCs to IBL and ORS

## Settings

We load the merged dataset :

```{r sobj_traj}
sobj_traj = readRDS(paste0(data_dir, "/4_zoom/4_zoom_hfsc_iblmors/hfsc_iblmors_sobj_traj_tinga.rds"))
sobj_traj
```


This is the projection name to visualize cells :

```{r sobj_name2D_traj}
name2D = "harmony_dm"
```

We load the trajectory object for visualisation purpose :

```{r load_traj}
my_traj = readRDS(paste0(data_dir, "/4_zoom/4_zoom_hfsc_iblmors/hfsc_iblmors_my_traj_tinga.rds"))
class(my_traj)
```


## Preparation

We defined cell type based on individual object :

```{r cluster_type_traj, fig.width = 7, fig.height = 5}
sobj_iblors$cell_bc = colnames(sobj_iblors)
sobj_traj$cell_bc = colnames(sobj_traj)
sobj_traj$cluster_type = dplyr::left_join(sobj_traj@meta.data[, c("cell_bc", "percent.mt")],
                                          sobj_iblors@meta.data[, c("cell_bc", "cluster_type")],
                                          by = "cell_bc")[, "cluster_type"] %>% as.character()
sobj_traj$cluster_type = ifelse(colnames(sobj_traj) %in% colnames(sobj_hfsc),
                                yes = "HFSC",
                                no = sobj_traj$cluster_type) %>%
  as.factor()
```

## Figures

Cells on the full atlas :

```{r fig_traj_location, fig.width = 2, fig.height = 2}
sobj$cell_bc = colnames(sobj)
sobj_traj$cell_bc = colnames(sobj_traj)
sobj$is_traj = dplyr::left_join(sobj@meta.data[, c("cell_bc", "percent.mt")],
                                sobj_traj@meta.data[, c("cell_bc", "cluster_type")],
                                by = "cell_bc")[, "cluster_type"]

Seurat::DimPlot(sobj, reduction = name2D_atlas, pt.size = 0.000001,
                group.by = "is_traj", order = levels(sobj_traj$cluster_type)) +
  ggplot2::scale_color_manual(values = c(color_markers[c("IBL", "ORS", "HFSC")], bg_color),
                              breaks = c("IBL", "ORS", "HFSC", NA), na.value = bg_color) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() + Seurat::NoLegend()
```


Project name :

```{r fig_traj_project_name, fig.width = 4, fig.height = 4}
# Random order
set.seed(1234)
rnd_order = sample(colnames(sobj_traj), replace = FALSE, size = ncol(sobj_traj))

# Extract coordinates
cells_coord = sobj_traj@reductions[[name2D]]@cell.embeddings %>%
  as.data.frame() %>%
  `colnames<-`(c("Dim1", "Dim2"))
cells_coord$sample_type = sobj_traj$sample_type
cells_coord = cells_coord[order(sobj_traj$sample_type), ]

# Plot
ggplot2::ggplot(cells_coord, aes(x = Dim1, y = Dim2, col = sample_type)) +
  ggplot2::geom_point(size = 1.2) +
  ggplot2::scale_color_manual(values = sample_type_colors,
                              breaks = names(sample_type_colors)) +
  ggplot2::theme_void() +
  ggplot2::theme(aspect.ratio = 1,
                 legend.position = "none")
```


Cluster type :

```{r fig_traj_cluster_type, fig.width = 3, fig.height = 3}
Seurat::DimPlot(sobj_traj, reduction = name2D, pt.size = 0.5,
                group.by = "cluster_type", cols = color_markers) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() +
  Seurat::NoLegend()
```

Pseudotime :

```{r fig_traj_pseudotime, fig.width = 6, fig.height = 4}
Seurat::FeaturePlot(sobj_traj, reduction = name2D, pt.size = 0.5,
                    features = "pseudotime") +
  ggplot2::scale_color_gradientn(colors = viridis::viridis(n = 100)) +
  ggplot2::lims(x = range(sobj_traj@reductions[[name2D]]@cell.embeddings[, 1]),
                y = range(sobj_traj@reductions[[name2D]]@cell.embeddings[, 2])) +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_blank()) +
  Seurat::NoAxes()
```

Pseudotime with dynplot's function :

```{r fig_traj_pseudotime_dynplot, fig.width = 5, fig.height = 5}
dynplot::plot_dimred(trajectory = my_traj,
                     dimred = sobj_traj[[name2D]]@cell.embeddings,
                     # Cells
                     color_cells = 'pseudotime',
                     size_cells = 1.6,
                     border_radius_percentage = 0,
                     # Trajectory
                     plot_trajectory = TRUE,
                     color_trajectory = "none",
                     label_milestones = FALSE,
                     size_milestones = 0,
                     size_transitions = 1)
```

# OEP002321 dataset

## Settings

We load the dataset containing all cells :

```{r load_sobj_wu}
sobj = readRDS(paste0(data_dir, "/5_wu/3_combined/wu_sobj.rds"))
sobj
```

This is the projection name to visualize cells :

```{r all_sobj_name2D_wu}
name2D = "harmony_38_tsne"
name2D_atlas = name2D
```

These are all the samples analyzed :

```{r sample_info_wu, class.source = 'fold-hide', fig.width = 8, fig.height = 3}
sample_info = readRDS(paste0(data_dir, "/5_wu/1_metadata/wu_sample_info.rds"))

# Nb cells by dataset
to_plot = table(sobj$sample_identifier) %>%
  as.data.frame.table(., stringsAsFactors = FALSE) %>%
  `colnames<-`(c("sample_identifier", "nb_cells")) %>%
  dplyr::left_join(x = ., y = sample_info, by = "sample_identifier") 

# patchwork
plot_list = aquarius::fig_plot_gb(to_plot, title = "Available datasets")
patchwork::wrap_plots(plot_list) +
  patchwork::plot_layout(design = "A\nB", heights = c(0.1,5)) &
  ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold", size = 15))
```

We define cluster type and cluster family :

```{r cluster_type_family_wu, fig.width = 7, fig.height = 5}
sobj$cell_type = sobj$cell_type %>%
  as.character() %>%
  factor(., levels = names(color_markers))

cluster_type = table(sobj$cell_type, sobj$seurat_clusters) %>%
  prop.table(., margin = 2) %>%
  apply(., 2, which.max)
cluster_type = setNames(nm = names(cluster_type),
                        levels(sobj$cell_type)[cluster_type])

sobj$cluster_type = cluster_type[sobj$seurat_clusters]
sobj$cluster_type = factor(sobj$cluster_type,
                           levels = levels(sobj$cell_type)) %>%
  base::droplevels()
sobj$cluster_family = custom_order_cell_type[sobj$cluster_type, "cell_family"]
sobj$cluster_family = factor(sobj$cluster_family,
                             levels = names(family_color))
```


## Global figures

Project name :

```{r figs2_sample_identifier, fig.width = 4, fig.height = 4}
# Random order
set.seed(1234)
rnd_order = sample(colnames(sobj), replace = FALSE, size = ncol(sobj))

# Extract coordinates
cells_coord = sobj@reductions[[name2D_atlas]]@cell.embeddings %>%
  as.data.frame() %>%
  `colnames<-`(c("Dim1", "Dim2"))
cells_coord$project_name = sobj$project_name
cells_coord = cells_coord[(rnd_order), ]

# Plot
ggplot2::ggplot(cells_coord, aes(x = Dim1, y = Dim2, col = project_name)) +
  ggplot2::geom_point(size = 0.5) +
  ggplot2::scale_color_manual(values = sample_info$color,
                              breaks = sample_info$project_name) +
  ggplot2::theme_void() +
  ggplot2::theme(aspect.ratio = 1,
                 legend.position = "none")
```

Cell type annotation :

```{r figs2_cell_type, fig.width = 4, fig.height = 4}
Seurat::DimPlot(sobj, reduction = name2D, pt.size = 0.5,
                group.by = "cell_type", cols = color_markers) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() +
  Seurat::NoLegend()
```

Cluster type annotation :

```{r figs2_cluster_type, fig.width = 4, fig.height = 4}
Seurat::DimPlot(sobj, reduction = name2D, pt.size = 0.5,
                group.by = "cluster_type", cols = color_markers) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() +
  Seurat::NoLegend()
```

Gene expression to assess annotation :

```{r figs2_gene_family, fig.width = 4, fig.height = 4}
genes = c("PTPRC", "MSX2", "KRT14")
names(genes) = c("immune cells", "matrix cells", "non-matrix cells")

plot_list = lapply(c(1:length(genes)), FUN = function(gene_id) {
  gene = genes[[gene_id]]
  pop = names(genes)[gene_id]
  
  sobj$my_gene = Seurat::FetchData(sobj, gene)[, 1] %>%
    aquarius::run_rescale(., new_min = 0, new_max = 10)
  
  Seurat::FeaturePlot(sobj, features = "my_gene", reduction = name2D_atlas) +
    ggplot2::scale_color_gradientn(colors = aquarius::color_gene,
                                   breaks = seq(0, 10, by = 2.5),
                                   labels = c("min", rep("", 3), "max")) +
    ggplot2::labs(title = gene) + 
    # subtitle = pop) +
    ggplot2::theme(aspect.ratio = 1,
                   plot.title = element_text(hjust = 0.5, size = 17),
                   plot.subtitle = element_text(hjust = 0.5, size = 15),
                   legend.text = element_text(size = 15),
                   legend.position = "none") +
    Seurat::NoAxes()
})

plot_list
```

Dotplot :

```{r figs2_dotplot, fig.width = 8, fig.height = 5, class.source = "fold-hide"}
custom_order_cell_type = custom_order_cell_type[levels(sobj$cluster_type), c("cell_type", "cell_family")]

plot_list = Seurat::DotPlot(sobj,
                            features = c("PTPRC",
                                         "CD3E", "CD4",
                                         "CD207", "AIF1",
                                         # "PRDM1", "KRT85",
                                         "MSX2",
                                         "KRT32", "KRT35",
                                         "KRT31", "PRR9",
                                         "BAMBI", "ALDH1A3",
                                         "KRT71", "KRT73",
                                         "TOP2A", "MCM5",
                                         "KRT14", "CXCL14",
                                         "KRT15", "COL17A1",
                                         "DIO2", "TCEAL2",
                                         "KRT16", "KRT6C",
                                         "SPINK5", "LY6D"),
                            group.by = "cluster_type", scale = TRUE,
                            scale.by = "radius", scale.min = NA, scale.max = NA) +
  ggplot2::scale_color_gradientn(colors = aquarius::color_gene) +
  ggplot2::theme(legend.position = "bottom",
                 legend.direction = "vertical",
                 # legend.justification = "bottom",
                 legend.box = "horizontal",
                 legend.box.margin = margin(0,25,0,0),
                 axis.title = element_blank(),
                 axis.ticks.y = element_blank(),
                 axis.text.y = element_blank(),
                 axis.line.y = element_blank(),
                 axis.text.x = element_text(angle = 45, hjust = 1, size = 11, color = "black"),
                 plot.margin = unit(c(0,0.5,0,0), "cm"))

p = ggplot2::ggplot(custom_order_cell_type, aes(y = cell_type, x = 0)) +
  ggplot2::geom_point(size = 0) +
  ggplot2::geom_segment(aes(y = 0.5, yend = 2.5, x = 0, xend = 0), size = 6, col = family_color["immune cells"]) +
  ggplot2::geom_segment(aes(y = 2.5, yend = 7.5, x = 0, xend = 0), size = 6, col = family_color["matrix"]) +
  ggplot2::geom_segment(aes(y = 7.5, yend = 11.5, x = 0, xend = 0), size = 6, col = family_color["non matrix"]) +
  ggplot2::scale_x_continuous(expand = c(0,0), limits = c(0,0)) +
  ggplot2::theme_classic() +
  ggplot2::theme(axis.text.x = element_blank(),
                 axis.ticks.x = element_blank(),
                 axis.title = element_blank(),
                 axis.line.x = element_blank(),
                 axis.text.y = element_text(size = 12, color = "black"),
                 plot.margin = unit(c(0.5,0,0,0.5), "cm"))

plot_list = patchwork::wrap_plots(p, plot_list,
                                  nrow = 1, widths = c(1, 25))
plot_list
```

## IBL and ORS dataset

We load the IBL + ORS dataset :

```{r sobj_iblors_wu}
sobj_iblors = readRDS(paste0(data_dir, "/5_wu/4_ibl_ors/iblmors_sobj.rds"))
sobj_iblors
```


This is the projection name to visualize cells :

```{r sobj_name2D_iblors_wu}
name2D = "harmony_20_tsne"
```

To represent results from differential expression, we load the analyses results :

```{r list_results_iblors_wu}
list_results = readRDS(paste0(data_dir, "/5_wu/4_ibl_ors/iblmors_list_results.rds"))

lapply(list_results, FUN = names)
```

We defined cluster type :

```{r cluster_type_iblors_wu, fig.width = 7, fig.height = 5}
cluster_type = table(sobj_iblors$cell_type, sobj_iblors$seurat_clusters) %>%
  prop.table(., margin = 2) %>%
  apply(., 2, which.max)
cluster_type = setNames(nm = names(cluster_type),
                        levels(sobj_iblors$cell_type)[cluster_type])

sobj_iblors$cluster_type = cluster_type[sobj_iblors$seurat_clusters]
sobj_iblors$cluster_type = factor(sobj_iblors$cluster_type,
                                  levels = c("IBL", "ORS"))
```

## IBL + ORS figure

IBL + ORS on the full atlas :

```{r figs2_iblors_location, fig.width = 1.5, fig.height = 1.5}
sobj$cell_bc = colnames(sobj)
sobj_iblors$cell_bc = colnames(sobj_iblors)
sobj$is_iblors = dplyr::left_join(sobj@meta.data[, c("cell_bc", "percent.mt")],
                                  sobj_iblors@meta.data[, c("cell_bc", "cluster_type")],
                                  by = "cell_bc")[, "cluster_type"]

Seurat::DimPlot(sobj, reduction = name2D_atlas, pt.size = 0.000001,
                group.by = "is_iblors", order = FALSE) +
  ggplot2::scale_color_manual(values = c(color_markers[c("IBL", "ORS")], bg_color),
                              breaks = c("IBL", "ORS", NA), na.value = bg_color) +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_blank()) +
  Seurat::NoAxes() + Seurat::NoLegend()
```

Cluster type :

```{r figs2_iblors_cluster_type, fig.width = 4, fig.height = 4}
Seurat::DimPlot(sobj_iblors, reduction = name2D, pt.size = 1,
                group.by = "cluster_type", cols = color_markers) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() +
  Seurat::NoLegend()
```


DE genes between IBL and ORS :

```{r figs2_iblors_de_pop, fig.width = 6, fig.height = 6}
mark = list_results$IBL_vs_ORS$mark
mark$gene_name = rownames(mark)
mark_label = rbind(
  # up-regulated in IBL
  mark %>% dplyr::top_n(., n = 20, wt = avg_logFC),
  # up-regulated in ORS
  mark %>% dplyr::top_n(., n = 20, wt = -avg_logFC),
  # representative and selective for IBL
  mark %>% dplyr::top_n(., n = 20, wt = (pct.1 - pct.2)),
  # representative and selective for ORS
  mark %>% dplyr::top_n(., n = 20, wt = -(pct.1 - pct.2))) %>%
  dplyr::distinct()
mark_label = mark_label[!grepl(rownames(mark_label), pattern = "^MT"), ]

avg_logFC_range = setNames(c(min(mark_label$avg_logFC), -1, 0, 1, max(mark_label$avg_logFC)),
                           nm = c("dodgerblue4", "dodgerblue3", "#B7B7B7", "firebrick3", "firebrick4"))


ggplot2::ggplot(mark, aes(x = pct.1, y = pct.2, col = avg_logFC)) +
  ggplot2::geom_abline(slope = 1, intercept = 0, lty = 2) +
  ggplot2::geom_point() +
  ggrepel::geom_label_repel(data = mark_label, max.overlaps = Inf,
                            aes(x = pct.1, y = pct.2, label = gene_name),
                            col = "black", fill = NA, size = 3.5, label.size = NA) +
  ggplot2::labs(x = "Enriched in IBL",
                y = "Enriched in ORS") +
  ggplot2::scale_color_gradientn(colors = names(avg_logFC_range),
                                 values = scales::rescale(unname(avg_logFC_range))) +
  ggplot2::theme_classic() +
  ggplot2::theme(aspect.ratio = 1)
```

GSEA plot :

```{r figs2_iblors_keratinization, fig.width = 6, fig.height = 4}
the_gs_name = "REACTOME_KERATINIZATION" 
the_content = list_results$IBL_vs_ORS$gsea@result %>%
  dplyr::filter(ID == the_gs_name)
the_subtitle = paste0("\nNES : ", round(the_content$NES, 2),
                      " | pvalue : ", round(the_content$pvalue, 4),
                      " | set size : ", the_content$setSize, " genes")

enrichplot::gseaplot2(x = list_results$IBL_vs_ORS$gsea,
                      geneSetID = the_gs_name) +
  ggplot2::labs(title = the_gs_name,
                subtitle = the_subtitle) +
  ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold",
                                           margin = ggplot2::margin(3, 3, 5, 3)),
                 plot.subtitle = ggtext::element_markdown(hjust = 0.5,
                                                          size = 10))
```

```{r figs2_iblors_ifna, fig.width = 6, fig.height = 4}
the_gs_name = "HALLMARK_INTERFERON_GAMMA_RESPONSE" 
the_content = list_results$IBL_vs_ORS$gsea@result %>%
  dplyr::filter(ID == the_gs_name)
the_subtitle = paste0("\nNES : ", round(the_content$NES, 2),
                      " | pvalue : ", round(the_content$pvalue, 4),
                      " | set size : ", the_content$setSize, " genes")

enrichplot::gseaplot2(x = list_results$IBL_vs_ORS$gsea,
                      geneSetID = the_gs_name) +
  ggplot2::labs(title = the_gs_name,
                subtitle = the_subtitle) +
  ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold",
                                           margin = ggplot2::margin(3, 3, 5, 3)),
                 plot.subtitle = ggtext::element_markdown(hjust = 0.5,
                                                          size = 10))
```

Genes of interest :

```{r figs2_iblors_genes, fig.width = 2, fig.height = 2}
genes = c("KRT16", "COL17A1", "DST", "KRT6B", "IL1R2", "WNT3",
          "IFI27", "CXCL14", "IGFBP3", "KRT15", "CD200")

plot_list = lapply(c(1:length(genes)), FUN = function(gene_id) {
  gene = genes[[gene_id]]
  
  sobj_iblors$my_gene = Seurat::FetchData(sobj_iblors, gene)[, 1] %>%
    aquarius::run_rescale(., new_min = 0, new_max = 10)
  
  Seurat::FeaturePlot(sobj_iblors, features = "my_gene", reduction = name2D, pt.size = 0.25) +
    ggplot2::scale_color_gradientn(colors = aquarius::color_gene,
                                   breaks = seq(0, 10, by = 2.5),
                                   labels = c("min", rep("", 3), "max")) +
    ggplot2::labs(title = gene) +
    ggplot2::theme(aspect.ratio = 1,
                   plot.title = element_text(hjust = 0.5, size = 17),
                   plot.subtitle = element_text(hjust = 0.5, size = 15),
                   legend.text = element_text(size = 15),
                   legend.position = "none") +
    Seurat::NoAxes()
})

plot_list
```

# R Session

```{r sessioninfo, echo = FALSE, fold_output = TRUE}
sessionInfo()
```

